Object Detection Evaluation 2012


The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png. For evaluation, we compute precision-recall curves for object detection and orientation-similarity-recall curves for joint object detection and orientation estimation. In the latter case not only the object 2D bounding box has to be located correctly, but also the orientation estimate in bird's eye view is evaluated. To rank the methods we compute average precision and average orientation similiarity. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

We evaluate object detection performance using the PASCAL criteria and object detection and orientation estimation performance using the measure discussed in our CVPR 2012 publication. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. Detections in don't care areas or detections which are smaller than the minimum size do not count as false positive. Difficulties are defined as follows:

  • Easy: Min. bounding box height: 40 Px, Max. occlusion level: Fully visible, Max. truncation: 15 %
  • Moderate: Min. bounding box height: 25 Px, Max. occlusion level: Partly occluded, Max. truncation: 30 %
  • Hard: Min. bounding box height: 25 Px, Max. occlusion level: Difficult to see, Max. truncation: 50 %

All methods are ranked based on the moderately difficult results. Note that for the hard evaluation ~2 % of the provided bounding boxes have not been recognized by humans, thereby upper bounding recall at 98 %. Hence, the hard evaluation is only given for reference.
Note 1: On 25.04.2017, we have fixed a bug in the object detection evaluation script. As of now, the submitted detections are filtered based on the min. bounding box height for the respective category which we have been done before only for the ground truth detections, thus leading to false positives for the category "Easy" when bounding boxes of height 25-39 Px were submitted (and to false positives for all categories if bounding boxes smaller than 25 Px were submitted). We like to thank Amy Wu, Matt Wilder, Pekka Jänis and Philippe Vandermersch for their feedback. The last leaderboards right before the changes can be found here!

Note 2: On 08.10.2019, we have followed the suggestions of the Mapillary team in their paper Disentangling Monocular 3D Object Detection and use 40 recall positions instead of the 11 recall positions proposed in the original Pascal VOC benchmark. This results in a more fair comparison of the results, please check their paper. The last leaderboards right before this change can be found here: Object Detection Evaluation, 3D Object Detection Evaluation, Bird's Eye View Evaluation.
Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
Additional information used by the methods
  • Stereo: Method uses left and right (stereo) images
  • Flow: Method uses optical flow (2 temporally adjacent images)
  • Multiview: Method uses more than 2 temporally adjacent images
  • Laser Points: Method uses point clouds from Velodyne laser scanner
  • Additional training data: Use of additional data sources for training (see details)

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 GraR-VoI 96.38 % 96.81 % 91.20 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
2 GraR-Po 96.18 % 96.84 % 91.11 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
3 SFD 96.17 % 98.97 % 91.13 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
4 VPFNet 96.15 % 96.64 % 91.14 % 0.06 s 2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. 2021.
5 Anonymous 96.12 % 98.74 % 91.12 % 0.1 s GPU @ 2.5 Ghz (Python)
6 Anonymous 96.12 % 99.07 % 91.12 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
7 CLOCs code 96.07 % 96.77 % 91.11 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
8 GraR-Vo 96.05 % 96.67 % 93.01 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
9 CLOCs_PVCas code 95.96 % 96.76 % 91.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
10 PE-RCVN 95.94 % 96.90 % 90.98 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
11 CityBrainLab 95.94 % 98.58 % 90.92 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
12 Anonymous 95.91 % 96.55 % 92.86 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
13 GraR-Pi 95.89 % 98.59 % 92.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
14 EA-M-RCNN(BorderAtt) 95.88 % 96.68 % 90.89 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
15 Anonymous 95.86 % 98.93 % 90.98 % n/a s 1 core @ 2.5 Ghz (C/C++)
16 Anonymous 95.79 % 96.69 % 92.75 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
17 SECOND 95.79 % 96.44 % 90.55 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
18 DVF-V 95.77 % 96.60 % 90.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
19 Fast-CLOCs 95.75 % 96.69 % 90.95 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
20 DSGN++
This method uses stereo information.
 95.70 % 98.08 % 88.27 % 0.4 s NVIDIA Tesla V100
21 TBD 95.69 % 96.33 % 93.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 CasA 95.62 % 96.52 % 92.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
23 BADet code 95.61 % 98.75 % 90.64 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition (PR) 2022.
24 SE-SSD
This method makes use of Velodyne laser scans.
 code 95.60 % 96.69 % 90.53 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
25 JPVNet 95.52 % 96.41 % 90.72 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
26 Anonymous 95.50 % 98.36 % 90.63 % 0.1s 1 core @ 2.5 Ghz (C/C++)
27 DVF-PV 95.49 % 96.42 % 92.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 SPANet 95.46 % 96.54 % 90.47 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
29 ISE-RCNN-PV 95.46 % 96.20 % 92.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
30 LGNet 95.43 % 96.52 % 92.73 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
31 TBD 95.43 % 96.06 % 90.38 % 0.1 s 1 core @ 2.5 Ghz (Python)
32 ISE-RCNN 95.43 % 96.38 % 92.82 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
33 SPG_mini
This method makes use of Velodyne laser scans.
 code 95.32 % 96.23 % 92.68 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
34 EQ-PVRCNN 95.32 % 98.23 % 92.65 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
35 anonymous 95.31 % 96.36 % 92.57 % 0.09 s GPU @ 2.5 Ghz (Python)
36 TBD 95.29 % 96.17 % 90.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
37 Anonymous code 95.28 % 96.30 % 92.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 DGDNH 95.24 % 98.36 % 92.69 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
39 PTA-RCNN 95.24 % 96.31 % 92.60 % 0.08 s 1 core @ 2.5 Ghz (Python)
40 VoxSeT 95.23 % 96.16 % 90.49 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
41 PC-CNN-V2
This method makes use of Velodyne laser scans.
 95.20 % 96.06 % 89.37 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
42 VPFNet code 95.17 % 96.06 % 92.66 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
43 F-PointNet
This method makes use of Velodyne laser scans.
 code 95.17 % 95.85 % 85.42 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
44 EPNet++ 95.17 % 96.73 % 92.10 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
45 SA-SSD code 95.16 % 97.92 % 90.15 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
46 Pyramid R-CNN 95.13 % 95.88 % 92.62 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
47 Voxel R-CNN code 95.11 % 96.49 % 92.45 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
48 3DSSD code 95.10 % 97.69 % 92.18 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
49 PV-RCNN++ 95.05 % 96.08 % 92.42 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
50 PDV 95.00 % 96.07 % 92.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
51 USVLab BSAODet (MM) 94.99 % 96.22 % 92.30 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
52 MVRA + I-FRCNN+ 94.98 % 95.87 % 82.52 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
53 SIENet code 94.97 % 96.02 % 92.40 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
54 VueronNet code 94.97 % 97.85 % 89.68 % 0.06 s 1 core @ 2.0 Ghz (Python)
55 VoTr-TSD code 94.94 % 95.97 % 92.44 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
56 GVNet-V2 94.92 % 96.29 % 92.21 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
57 CSVoxel-RCNN 94.91 % 96.33 % 92.11 % 0.03 s GPU @ 1.0 Ghz (Python)
58 GVNet code 94.86 % 96.30 % 92.22 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
59 TBD 94.85 % 97.04 % 92.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
60 M3DeTR code 94.83 % 97.39 % 92.10 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
61 StructuralIF 94.81 % 96.14 % 92.12 % 0.02 s 8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
62 SRIF-RCNN 94.79 % 95.63 % 92.35 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
63 ST-RCNN
This method makes use of Velodyne laser scans.
 94.79 % 98.06 % 92.12 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
64 ST-RCNN (SNLW-RCNN)
This method makes use of Velodyne laser scans.
 code 94.79 % 98.06 % 92.12 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
65 TPCG 94.78 % 95.96 % 92.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 DCAnet 94.77 % 95.75 % 92.27 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
67 VCRCNN 94.77 % 96.06 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 XView 94.77 % 95.89 % 92.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
69 SCIR-Net
This method makes use of Velodyne laser scans.
 94.76 % 96.15 % 91.99 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
70 MSG-PGNN 94.75 % 95.86 % 92.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
71 TBD 94.74 % 95.88 % 91.96 % 0.06 s GPU @ 2.5 Ghz (Python)
72 P2V-RCNN 94.73 % 96.03 % 92.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
73 FusionDetv2-v4 94.73 % 95.94 % 92.00 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
74 Generalized-SIENet 94.72 % 95.76 % 92.19 % 0.08 s 1 core @ 2.5 Ghz (Python)
75 SqueezeRCNN 94.72 % 96.02 % 92.12 % 0.08 s 1 core @ 2.5 Ghz (Python)
76 SPG
This method makes use of Velodyne laser scans.
 code 94.71 % 97.80 % 92.19 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
77 CAT-Det 94.71 % 95.97 % 92.07 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
78 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 94.70 % 98.17 % 92.04 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
79 HyBrid Feature Det 94.69 % 95.89 % 92.11 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
80 SVGA-Net 94.67 % 96.05 % 91.86 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
81 DDet 94.66 % 95.82 % 92.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 FPV-SSD 94.66 % 96.92 % 91.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
83 LZY_RCNN 94.65 % 95.81 % 92.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
84 FusionDetv2-v3 94.64 % 96.16 % 91.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
85 TransCyclistNet 94.64 % 96.08 % 92.10 % 0.08 s 1 core @ 2.5 Ghz (Python)
86 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 94.64 % 95.86 % 92.10 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
87 Fast VP-RCNN code 94.62 % 98.00 % 91.91 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
88 TransDet3D 94.61 % 95.83 % 92.07 % 0.08 s 1 core @ 2.5 Ghz (Python)
89 RangeIoUDet
This method makes use of Velodyne laser scans.
 94.61 % 95.74 % 91.98 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
90 Point Image Fusion 94.61 % 95.70 % 92.11 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
91 USVLab BSAODet (SM) 94.60 % 96.10 % 90.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
92 SA-voxel-centernet code 94.59 % 95.80 % 92.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
93 DVFENet 94.57 % 95.35 % 91.77 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
94 WHUT-iou_ssd code 94.54 % 95.77 % 91.91 % 0.045s 1 core @ 2.5 Ghz (C/C++)
95 sa-voxel-centernet code 94.53 % 95.88 % 92.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
96 anonymous code 94.53 % 97.51 % 91.80 % 0.05s 1 core @ >3.5 Ghz (python)
97 FPC3D
This method makes use of the epipolar geometry.
 94.52 % 96.06 % 91.72 % 33 s 1 core @ 2.5 Ghz (C/C++)
98 FPC-RCNN 94.51 % 96.15 % 91.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
99 SPVB-SSD 94.49 % 95.80 % 91.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
100 TuSimple code 94.47 % 95.12 % 86.45 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
101 EPNet code 94.44 % 96.15 % 89.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
102 SERCNN
This method makes use of Velodyne laser scans.
 94.42 % 96.33 % 89.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
103 SAA-SECOND 94.39 % 95.67 % 91.63 % 38m s 1 core @ 2.5 Ghz (C/C++)
104 TCDVF 94.31 % 95.02 % 91.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
105 UberATG-MMF
This method makes use of Velodyne laser scans.
 94.25 % 97.41 % 89.87 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
106 SRDL 94.24 % 95.86 % 91.80 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
107 FusionDetv1 94.23 % 95.84 % 91.80 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
108 TBD 94.22 % 95.00 % 91.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
109 FusionDetv2-v2 94.21 % 95.75 % 89.89 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
110 SARFE 94.18 % 95.74 % 91.57 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
111 FPCR-CNN 94.13 % 95.95 % 91.20 % 0.05 s 1 core @ 2.5 Ghz (Python)
112 SAA-PV-RCNN 94.11 % 95.01 % 92.50 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
113 NV2P-RCNN 94.07 % 97.82 % 91.20 % 0.1 s GPU @ 2.5 Ghz (Python)
114 DGT-Det3D 94.03 % 95.55 % 91.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
115 RangeRCNN
This method makes use of Velodyne laser scans.
 94.03 % 95.48 % 91.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
116 VueronNet 94.03 % 96.70 % 87.58 % 0.08 s GPU @ 2.5 Ghz (Python)
117 DGIST MT-CNN 94.00 % 95.25 % 86.76 % 0.09 s GPU @ 1.0 Ghz (Python)
118 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 93.99 % 95.81 % 91.72 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
119 DD3D code 93.99 % 94.69 % 89.37 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
120 SIF 93.95 % 95.51 % 91.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
121 TBD 93.89 % 94.52 % 86.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
122 MGAF-3DSSD code 93.87 % 94.45 % 86.37 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
123 LPCG-Monoflex 93.86 % 96.90 % 83.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
124 MMLAB LIGA-Stereo
This method uses stereo information.
 code 93.82 % 96.43 % 86.19 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
125 DKAnet 93.79 % 95.16 % 89.27 % 0.05 s 1 core @ 2.0 Ghz (Python)
126 LIGA-Stereo-old
This method uses stereo information.
 93.77 % 96.66 % 83.97 % 0.375 s Titan Xp
127 Associate-3Ddet_v2 93.77 % 96.83 % 88.57 % 0.04 s 1 core @ 2.5 Ghz (Python)
128 Patches - EMP
This method makes use of Velodyne laser scans.
 93.75 % 97.91 % 90.56 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
129 CAD 93.73 % 96.84 % 88.74 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
130 CIA-SSD
This method makes use of Velodyne laser scans.
 code 93.72 % 96.87 % 86.20 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
131 QD-3DT
This is an online method (no batch processing).
 code 93.66 % 94.26 % 83.63 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
132 MVAF-Net code 93.66 % 95.37 % 90.90 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
133 SA3DNet
This method uses stereo information.
This method makes use of Velodyne laser scans.
 93.62 % 96.57 % 88.65 % 0.05 s GPU @ 2.5 Ghz (Python)
134 FusionDetv2-v5 93.61 % 95.33 % 89.22 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
135 KpNet 93.60 % 96.76 % 85.98 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
136 KpNet 93.60 % 96.74 % 85.97 % 42 s 1 core @ 2.5 Ghz (C/C++)
137 AM-SSD 93.58 % 96.78 % 90.61 % 0.04 s 1 core @ 2.5 Ghz (Python)
138 TF3D
This method makes use of Velodyne laser scans.
 93.57 % 96.59 % 88.64 % 0.1 s 2 cores @ 3.0 Ghz (Python)
139 DVF 93.57 % 96.46 % 88.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
140 IA-SSD (multi) 93.56 % 96.10 % 90.68 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
141 MonoPair 93.55 % 96.61 % 83.55 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
142 IA-SSD (single) 93.54 % 96.26 % 88.49 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
143 EBM3DOD code 93.54 % 96.81 % 88.33 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
144 Deep MANTA 93.50 % 98.89 % 83.21 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
145 Point-GNN
This method makes use of Velodyne laser scans.
 code 93.50 % 96.58 % 88.35 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
146 BtcDet
This method makes use of Velodyne laser scans.
 code 93.47 % 96.23 % 88.55 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
147 Struc info fusion II 93.45 % 96.72 % 88.31 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
148 EBM3DOD baseline code 93.45 % 96.72 % 88.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
149 TBD
This method makes use of Velodyne laser scans.
 93.43 % 96.52 % 90.71 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
150 RRC code 93.40 % 95.68 % 87.37 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
151 Sem-Aug v1 code 93.39 % 96.39 % 90.70 % 0.04 s GPU @ 3.5 Ghz (Python)
152 TBD 93.38 % 94.17 % 88.39 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
153 3D-CVF at SPA
This method makes use of Velodyne laser scans.
 93.36 % 96.78 % 86.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
154 DSASNet 93.33 % 96.55 % 88.47 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
155 TBD 93.33 % 96.02 % 90.50 % TBD GPU @ 2.5 Ghz (Python + C/C++)
156 FS-Net
This method makes use of Velodyne laser scans.
 93.32 % 96.39 % 90.64 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
157 Struc info fusion I 93.31 % 96.59 % 88.23 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
158 VPN 93.30 % 96.19 % 88.30 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
159 CityBrainLab-CT3D code 93.30 % 96.28 % 90.58 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
160 STD code 93.22 % 96.14 % 90.53 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
161 SARPNET 93.21 % 96.07 % 88.09 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
162 demo 93.21 % 96.16 % 90.23 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
163 H^23D R-CNN code 93.20 % 96.20 % 90.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
164 Fast Point R-CNN
This method makes use of Velodyne laser scans.
 93.18 % 96.13 % 87.68 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
165 ASCNet 93.17 % 96.09 % 90.43 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
166 VCT 93.17 % 96.31 % 90.52 % 0.2 s 1 core @ 2.5 Ghz (Python)
167 sensekitti code 93.17 % 94.79 % 84.38 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
168 Sem-Aug-PointRCNN code 93.17 % 95.78 % 88.35 % 0.1 s GPU @ 3.5 Ghz (C/C++)
169 MVOD 93.16 % 96.17 % 92.56 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
170 MBDF-Net 93.15 % 96.26 % 90.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
171 SJTU-HW 93.11 % 96.30 % 82.21 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
172 KAIST-VDCLab 93.08 % 96.27 % 85.66 % 0.04 s 1 core @ 2.5 Ghz (Python)
173 SGNet 93.08 % 96.43 % 90.53 % 0.09 s GPU @ 2.5 Ghz (Python)
174 FromVoxelToPoint code 93.06 % 96.08 % 90.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
175 MSADet 92.95 % 96.18 % 89.95 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
176 CLOCs_SecCas 92.95 % 95.43 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
177 MBDF-Net-1 92.85 % 95.98 % 89.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
178 YF 92.85 % 96.04 % 89.96 % 0.04 s GPU @ 2.5 Ghz (C/C++)
179 MDNet 92.82 % 96.14 % 85.37 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
180 HotSpotNet 92.81 % 96.21 % 89.80 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
181 SegVoxelNet 92.73 % 96.00 % 87.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
182 Patches
This method makes use of Velodyne laser scans.
 92.72 % 96.34 % 87.63 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
183 CenterNet3D 92.69 % 95.76 % 89.81 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
184 R-GCN 92.67 % 96.19 % 87.66 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
185 TBD 92.66 % 95.60 % 90.55 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
186 PI-RCNN 92.66 % 96.17 % 87.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
187 PointPainting
This method makes use of Velodyne laser scans.
 92.58 % 98.39 % 89.71 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
188 DASS 92.53 % 96.23 % 87.75 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
189 NV-RCNN 92.51 % 95.86 % 89.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
190 3D IoU-Net 92.47 % 96.31 % 87.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
191 CSNet 92.46 % 95.99 % 89.24 % 0.1 s 1 core @ 2.5 Ghz (Python)
192 Associate-3Ddet code 92.45 % 95.61 % 87.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
193 S-AT GCN 92.44 % 95.06 % 90.78 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
194 LazyTorch-CP-Small-P 92.38 % 94.71 % 90.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
195 3D-VDNet 92.35 % 95.42 % 89.37 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
196 LazyTorch-CP-Infer-O 92.35 % 94.76 % 90.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
197 PointRGCN 92.33 % 97.51 % 87.07 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
198 CVFNet 92.27 % 95.61 % 88.75 % 28.1ms 1 core @ 2.5 Ghz (Python)
199 F-ConvNet
This method makes use of Velodyne laser scans.
 code 92.19 % 95.85 % 80.09 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
200 PFF3D
This method makes use of Velodyne laser scans.
 code 92.15 % 95.37 % 87.54 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
201 SDP+RPN 92.03 % 95.16 % 79.16 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
202 RangeDet code 92.03 % 95.20 % 87.14 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
203 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 92.00 % 95.88 % 86.98 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
204 CrazyTensor-CP 91.95 % 93.64 % 89.23 % 1 s 1 core @ 2.5 Ghz (Python)
205 Dune-DCF-e09 91.90 % 94.97 % 88.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
206 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 91.90 % 95.92 % 87.11 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
207 CrazyTensor-CF 91.89 % 94.98 % 88.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
208 AutoAlign 91.87 % 95.15 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
209 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 91.86 % 95.03 % 89.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
210 WeakM3D 91.81 % 94.51 % 85.35 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
211 epBRM
This method makes use of Velodyne laser scans.
 code 91.77 % 94.59 % 88.45 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
212 TBD 91.75 % 94.53 % 86.54 % 0.1 s 1 core @ 2.5 Ghz (Python)
213 C-GCN 91.73 % 95.64 % 86.37 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
214 ITVD code 91.73 % 95.85 % 79.31 % 0.3 s GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
215 sscl-20p 91.71 % 97.14 % 88.72 % 0.02 s 1 core @ 2.5 Ghz (Python)
216 Dune-DCF-e11 91.68 % 94.69 % 88.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
217 City-CF-fixed 91.67 % 94.87 % 88.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
218 Dune-DCF-e15 91.67 % 94.72 % 88.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
219 SINet+ code 91.67 % 94.17 % 78.60 % 0.3 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
220 SFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 91.64 % 94.85 % 82.13 % 0.04 s GPU @ 2.5 Ghz (Python)
221 HS3D code 91.62 % 95.51 % 86.94 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
222 mono3d 91.60 % 94.60 % 84.86 % 0.03 s GPU @ 2.5 Ghz (Python)
223 Cascade MS-CNN code 91.60 % 94.26 % 78.84 % 0.25 s GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019.
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
224 GFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 91.59 % 94.55 % 81.80 % 0.07 s GPU @ 2.5 Ghz (Python)
225 LazyTorch-CP 91.58 % 93.99 % 89.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
226 TBD 91.56 % 94.52 % 86.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
227 CA3D 91.48 % 95.19 % 82.01 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
228 PointRGBNet 91.48 % 95.40 % 86.50 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
229 MAFF-Net(DAF-Pillar) 91.46 % 94.38 % 83.89 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
230 AIMC-RUC 91.45 % 96.94 % 86.28 % 0.11 s 1 core @ 2.5 Ghz (Python)
231 HRI-VoxelFPN 91.44 % 96.65 % 86.18 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
232 FPC3D_all
This method makes use of Velodyne laser scans.
 91.42 % 95.52 % 86.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
233 EgoNet code 91.39 % 96.18 % 81.33 % 0.1 s GPU @ 1.5 Ghz (Python)
S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
234 HNet-3DSSD
This method makes use of Velodyne laser scans.
 code 91.35 % 94.91 % 87.47 % 0.05 s GPU @ 2.5 Ghz (Python)
235 City-CF 91.35 % 94.54 % 88.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
236 PP-PCdet code 91.32 % 94.82 % 88.18 % 0.01 s 1 core @ 2.5 Ghz (Python)
237 Contrastive PP code 91.29 % 96.93 % 88.11 % 0.01 s 1 core @ 2.5 Ghz (Python)
238 Stereo CenterNet
This method uses stereo information.
 91.27 % 96.61 % 83.50 % 0.04 s GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
239 PointPillars
This method makes use of Velodyne laser scans.
 code 91.19 % 94.00 % 88.17 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
240 LTN 91.18 % 94.68 % 81.51 % 0.4 s GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
241 WS3D
This method makes use of Velodyne laser scans.
 91.15 % 95.13 % 86.52 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
242 KM3D code 91.07 % 96.44 % 81.19 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
243 3D_att
This method makes use of Velodyne laser scans.
 91.05 % 96.70 % 85.88 % 0.17 s GPU @ 2.5 Ghz (Python)
244 Geo3D 91.04 % 94.28 % 78.91 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
245 FII-CenterNet 91.03 % 94.48 % 83.00 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
246 Aston-EAS 91.02 % 93.91 % 77.93 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
247 MonoFlex 91.02 % 96.01 % 83.38 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
248 Mix-Teaching-M3D 91.02 % 96.35 % 83.41 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
249 SCSTSV-MonoFlex 90.99 % 96.44 % 81.16 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
250 ARPNET 90.99 % 94.00 % 83.49 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
251 GA-Aug 90.92 % 93.93 % 83.03 % 0.04 s GPU @ 2.5 Ghz (Python)
252 MonoEF code 90.88 % 96.32 % 83.27 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
253 PatchNet code 90.87 % 93.82 % 79.62 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
254 MV3D
This method makes use of Velodyne laser scans.
 90.83 % 96.47 % 78.63 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
255 MonoDistill 90.81 % 95.92 % 81.08 % 0.04 s 1 core @ 2.5 Ghz (Python)
256 monodle code 90.81 % 93.83 % 80.93 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
257 3D IoU Loss
This method makes use of Velodyne laser scans.
 90.79 % 95.92 % 85.65 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
258 SINet_VGG code 90.79 % 93.59 % 77.53 % 0.2 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
259 APL-Second 90.70 % 95.82 % 85.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
260 vadin-TBD 90.69 % 95.92 % 80.91 % 0.04 s 1 core @ 2.5 Ghz (Python)
261 TANet code 90.67 % 93.67 % 85.31 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
262 MF 90.66 % 93.57 % 86.15 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
263 MonoGround 90.63 % 93.94 % 80.80 % 0.03 s 1 core @ 2.5 Ghz (Python)
264 NF2 90.62 % 94.14 % 81.30 % 0.1 s GPU @ 2.5 Ghz (Python)
265 MonoEdge 90.62 % 93.52 % 80.91 % 0.05 s GPU @ 2.5 Ghz (Python)
266 MonoCInIS 90.60 % 96.05 % 82.43 % 0,13 s GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
267 MonoFlex 90.52 % 95.59 % 83.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
268 CG-Stereo
This method uses stereo information.
 90.38 % 96.31 % 82.80 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
269 MonoEdge-Rotate 90.30 % 93.53 % 80.60 % 0.05 s GPU @ 2.5 Ghz (Python)
270 SCNet
This method makes use of Velodyne laser scans.
 90.30 % 95.59 % 85.09 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
271 PS-fld 90.27 % 95.75 % 82.32 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
272 Deep3DBox 90.19 % 94.71 % 76.82 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
273 FQNet 90.17 % 94.72 % 76.78 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
274 MonoGeo 90.14 % 95.11 % 80.19 % 0.05 s 1 core @ 2.5 Ghz (Python)
275 DeepStereoOP 90.06 % 95.15 % 79.91 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
276 SubCNN 89.98 % 94.26 % 79.78 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
277 MLOD
This method makes use of Velodyne laser scans.
 code 89.97 % 94.88 % 84.98 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
278 GPP code 89.96 % 94.02 % 81.13 % 0.23 s GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.
279 AVOD
This method makes use of Velodyne laser scans.
 code 89.88 % 95.17 % 82.83 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
280 SINet_PVA code 89.86 % 92.72 % 76.47 % 0.11 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
281 Digging_M3D 89.77 % 93.73 % 79.68 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
282 TBD_BD code 89.73 % 94.18 % 86.84 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
283 3DOP
This method uses stereo information.
 code 89.55 % 92.96 % 79.38 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
284 IAFA 89.46 % 93.08 % 79.83 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
285 Mono3D code 89.37 % 94.52 % 79.15 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
286 4d-MSCNN
This method uses stereo information.
 code 89.37 % 92.40 % 77.00 % 0.3 min GPU @ 3.0 Ghz (Matlab + C/C++)
P. Ferraz, B. Oliveira, F. Ferreira, C. Silva Martins and others: Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision. IET Intelligent Transport Systems 2020.
287 MonoDDE 89.19 % 96.76 % 81.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
288 PPTrans 89.17 % 95.17 % 81.77 % 0.2 s GPU @ 2.5 Ghz (Python)
289 FusionDetv2-v1 89.10 % 94.82 % 84.28 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
290 M3DSSD++ code 89.06 % 94.94 % 77.17 % 0.16s 1 core @ 2.5 Ghz (C/C++)
291 GAC3D++ 88.93 % 94.23 % 79.09 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
292 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 88.92 % 94.70 % 84.13 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
293 Keypoint-3D 88.87 % 93.31 % 76.10 % 14 s 1 core @ 2.5 Ghz (C/C++)
294 PCT code 88.78 % 96.45 % 78.85 % 0.045 s 1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
295 FusionDetv2-baseline 88.76 % 94.28 % 85.65 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
296 Lite-FPN-GUPNet 88.76 % 96.45 % 76.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
297 AM3D 88.71 % 92.55 % 77.78 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
298 MS-CNN code 88.68 % 93.87 % 76.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
299 MM-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 88.63 % 93.74 % 78.56 % 0.04 s GPU @ 2.5 Ghz (Python)
300 MonoPSR code 88.50 % 93.63 % 73.36 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
301 Shift R-CNN (mono) code 88.48 % 94.07 % 78.34 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
302 RCD 88.46 % 92.52 % 83.73 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
303 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 88.46 % 95.54 % 78.14 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
304 MonoDTR 88.41 % 93.90 % 76.20 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
305 CMKD 88.41 % 95.28 % 81.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
306 CMKD 88.40 % 95.03 % 81.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
307 MonoLCD 88.33 % 93.74 % 78.59 % 0.04 s 1 core @ 2.5 Ghz (Python)
308 3DBN
This method makes use of Velodyne laser scans.
 88.29 % 93.74 % 80.74 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
309 MonoCon code 88.22 % 93.59 % 76.18 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
310 MonoCInIS 88.16 % 96.22 % 75.72 % 0,14 s GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
311 MonoRUn code 87.91 % 95.48 % 78.10 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
312 SAIC_ADC_Mono3D code 87.78 % 95.21 % 80.14 % 50 s GPU @ 2.5 Ghz (Python)
313 SMOKE code 87.51 % 93.21 % 77.66 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
314 CDN
This method uses stereo information.
 code 87.19 % 95.85 % 79.43 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
315 EG_DETR 87.10 % 93.04 % 79.05 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
316 mono3d code 87.07 % 93.28 % 77.72 % TBD TBD
317 RTM3D code 86.93 % 91.82 % 77.41 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
318 MonoRCNN code 86.78 % 91.98 % 66.97 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
319 Anonymous code 86.77 % 94.34 % 71.93 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
320 BirdNet+
This method makes use of Velodyne laser scans.
 code 86.73 % 92.61 % 81.80 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
321 UPF_3D
This method uses stereo information.
 86.61 % 96.12 % 79.53 % 0.29 s 1 core @ 2.5 Ghz (Python)
322 MP-Mono 86.54 % 90.66 % 65.72 % 0.16 s GPU @ 2.5 Ghz (Python)
323 ANM 86.45 % 94.32 % 76.54 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
324 GUPNet code 86.45 % 94.15 % 74.18 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
325 GCDR 86.45 % 94.15 % 74.18 % 0.28 s 1 core @ 2.5 Ghz (Python)
326 DSGN
This method uses stereo information.
 code 86.43 % 95.53 % 78.75 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
327 gupnet_se 86.33 % 94.27 % 74.08 % 0.03s 1 core @ 2.5 Ghz (C/C++)
328 TBD 86.03 % 91.50 % 78.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
329 Stereo R-CNN
This method uses stereo information.
 code 85.98 % 93.98 % 71.25 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
330 StereoFENet
This method uses stereo information.
 85.70 % 91.48 % 77.62 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
331 City-ICN 85.70 % 93.91 % 73.41 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
332 DisposalNet
This method uses stereo information.
 85.49 % 89.50 % 82.52 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
333 LT-M3OD 85.42 % 93.99 % 75.77 % 0.03 s 1 core @ 2.5 Ghz (Python)
334 ResNet-RRC_Car 85.33 % 91.45 % 74.27 % 0.06 s GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
335 AEC3D 85.22 % 90.74 % 80.82 % 18 ms GPU @ 2.5 Ghz (Python)
336 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
 code 85.15 % 94.95 % 77.78 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
337 M3D-RPN code 85.08 % 89.04 % 69.26 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
338 CDN-PL++
This method uses stereo information.
 85.01 % 94.66 % 77.60 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
339 SDP+CRC (ft) 85.00 % 92.06 % 71.71 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
340 LPCG-M3D 84.95 % 87.35 % 77.05 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
341 SS3D 84.92 % 92.72 % 70.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
342 VN3D 84.80 % 90.73 % 78.41 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
343 ZongmuMono3d code 84.64 % 93.06 % 75.29 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
344 MonoFENet 84.63 % 91.68 % 76.71 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
345 DLE code 84.45 % 94.66 % 62.10 % 0.06 s NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
346 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
 84.39 % 93.08 % 79.27 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
347 Complexer-YOLO
This method makes use of Velodyne laser scans.
 84.16 % 91.92 % 79.62 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
348 ZoomNet
This method uses stereo information.
 code 83.92 % 94.22 % 69.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
349 OSE+ 83.92 % 95.20 % 76.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
350 MK3D 83.75 % 94.07 % 71.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
351 D4LCN code 83.67 % 90.34 % 65.33 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
352 Faster R-CNN code 83.16 % 88.97 % 72.62 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
353 ppt 83.13 % 85.27 % 77.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
354 SGM3D 83.05 % 93.66 % 73.35 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
355 Pseudo-LiDAR++
This method uses stereo information.
 code 82.90 % 94.46 % 75.45 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
356 Disp R-CNN
This method uses stereo information.
 code 82.86 % 93.64 % 68.33 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
357 BS3D 82.72 % 95.35 % 70.01 % 22 ms Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
358 CrazyTensor-ICN 82.70 % 90.79 % 70.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
359 Disp R-CNN (velo)
This method uses stereo information.
 code 82.64 % 93.45 % 70.45 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
360 HBD 82.64 % 93.13 % 75.14 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
361 vadin-TBD2 code 82.54 % 92.81 % 72.80 % 0.20 s 1 core @ 2.5 Ghz (Python)
362 YOLOStereo3D
This method uses stereo information.
 code 82.15 % 94.81 % 62.17 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
363 Ground-Aware code 82.05 % 92.33 % 62.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
364 FRCNN+Or code 82.00 % 92.91 % 68.79 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
365 SwinMono3D 81.71 % 91.99 % 61.78 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
366 DDMP-3D 81.70 % 91.15 % 63.12 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
367 SD3DOD 81.64 % 92.60 % 75.97 % 0.04 s GPU @ 2.5 Ghz (Python)
368 ITS-MDPL 81.56 % 92.61 % 74.23 % 0.16 s GPU @ 2.5 Ghz (Python)
369 A3DODWTDA (image) code 81.25 % 78.96 % 70.56 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
370 SOD 81.18 % 94.24 % 66.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
371 none 81.07 % 91.14 % 68.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
372 RefineNet 81.01 % 91.91 % 65.67 % 0.20 s GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016.
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
373 K3D 80.86 % 93.58 % 71.18 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
374 CaDDN code 80.73 % 93.61 % 71.09 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
375 EGFN
This method uses stereo information.
 80.58 % 93.07 % 70.68 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
376 PGD-FCOS3D code 80.58 % 92.04 % 69.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
377 EW code 80.54 % 87.53 % 67.16 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
378 GrooMeD-NMS code 80.28 % 90.14 % 63.78 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
379 3D-GCK 80.19 % 89.55 % 68.08 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
380 COF3D 80.16 % 87.85 % 61.97 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
381 EM code 80.05 % 87.00 % 66.77 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
382 KMC code 79.99 % 89.71 % 73.33 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
383 RelationNet3D_dla34 code 79.78 % 83.69 % 69.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
384 Lite-FPN 79.65 % 87.04 % 65.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
385 YoloMono3D code 79.63 % 92.37 % 59.69 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
386 E2E-DA 79.40 % 92.12 % 69.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
387 A3DODWTDA
This method makes use of Velodyne laser scans.
 code 79.15 % 82.98 % 68.30 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
388 ImVoxelNet code 79.09 % 89.80 % 69.45 % 0.2 s GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
389 DFR-Net 78.81 % 90.13 % 60.40 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
390 spLBP 78.66 % 81.66 % 61.69 % 1.5 s 8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
391 MonoHMOO 78.21 % 92.33 % 61.58 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
392 E2E-DA-Lite (Res18) 78.21 % 90.79 % 66.16 % 0.01 s GPU @ 2.5 Ghz (Python)
393 3D-SSMFCNN code 78.19 % 77.92 % 69.19 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
394 MonoGRNet code 77.94 % 88.65 % 63.31 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
395 Aug3D-RPN 77.88 % 85.57 % 61.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
396 AutoShape code 77.66 % 86.51 % 64.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
397 Reinspect code 77.48 % 90.27 % 66.73 % 2s 1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
398 multi-task CNN 77.18 % 86.12 % 68.09 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
399 Regionlets 76.99 % 88.75 % 60.49 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
400 3DVP code 76.98 % 84.95 % 65.78 % 40 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
401 RelationNet3D_res18 code 76.96 % 87.14 % 67.49 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
402 AutoShape 76.82 % 83.75 % 63.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
403 Mobile Stereo R-CNN
This method uses stereo information.
 76.73 % 90.08 % 62.23 % 1.8 s NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
404 RelationNet3D 76.62 % 81.36 % 68.48 % 0.04 s GPU @ 2.5 Ghz (Python)
405 ICCV 76.45 % 85.48 % 65.52 % 0.04 s GPU @ 2.5 Ghz (Python)
406 SubCat code 76.36 % 84.10 % 60.56 % 0.7 s 6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
407 GS3D 76.35 % 86.23 % 62.67 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
408 AOG code 76.24 % 86.08 % 61.51 % 3 s 4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
409 Pose-RCNN 75.83 % 89.59 % 64.06 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
410 3D FCN
This method makes use of Velodyne laser scans.
 74.65 % 86.74 % 67.85 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
411 OC Stereo
This method uses stereo information.
 code 74.60 % 87.39 % 62.56 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
412 MAOLoss code 73.79 % 89.31 % 63.59 % 0.05 s 1 core @ 2.5 Ghz (Python)
413 Kinematic3D code 71.73 % 89.67 % 54.97 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
414 AOG-View 71.26 % 85.01 % 55.73 % 3 s 1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
415 BEVC 70.93 % 79.97 % 64.46 % 35ms GPU @ 1.5 Ghz (Python)
416 GAC3D 70.73 % 83.30 % 52.23 % 0.25 s 1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
417 MV-RGBD-RF
This method makes use of Velodyne laser scans.
 70.70 % 77.89 % 57.41 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
418 Vote3Deep
This method makes use of Velodyne laser scans.
 70.30 % 78.95 % 63.12 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
419 ROI-10D 70.16 % 76.56 % 61.15 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
420 RetinaMono 69.83 % 74.54 % 60.95 % 0.02 s 1 core @ 2.5 Ghz (Python)
421 TBD 68.30 % 88.62 % 59.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
422 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 68.05 % 92.10 % 65.61 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
423 Decoupled-3D 67.92 % 87.78 % 54.53 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
424 SparVox3D 67.88 % 83.76 % 52.56 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
425 MDSNet 67.79 % 90.97 % 53.39 % 0.07 s 1 core @ 2.5 Ghz (Python)
426 Pseudo-Lidar
This method uses stereo information.
 code 67.79 % 85.40 % 58.50 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
427 OC-DPM 67.06 % 79.07 % 52.61 % 10 s 8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
428 DPM-VOC+VP 66.72 % 82.15 % 49.01 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
429 BdCost48LDCF code 66.63 % 81.38 % 52.20 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
430 MM 65.65 % 88.80 % 56.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
431 RefinedMPL 65.24 % 88.29 % 53.20 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
432 MDPM-un-BB 64.06 % 79.74 % 49.07 % 60 s 4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
433 Y4 code 63.60 % 81.79 % 56.20 % 0.03 s 1 core @ 2.5 Ghz (Python)
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434 TLNet (Stereo)
This method uses stereo information.
 code 63.53 % 76.92 % 54.58 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
435 PDV-Subcat 63.24 % 78.27 % 47.67 % 7 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
436 MODet
This method makes use of Velodyne laser scans.
 62.54 % 66.06 % 60.04 % 0.05 s GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
437 Graph-NMS 61.93 % 78.55 % 53.72 % 36 ms GPU @ 2.5 Ghz (Python)
438 SubCat48LDCF code 61.16 % 78.86 % 44.69 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
439 DPM-C8B1
This method uses stereo information.
 60.21 % 75.24 % 44.73 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
440 SAMME48LDCF code 58.38 % 77.47 % 44.43 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
441 LSVM-MDPM-sv 58.36 % 71.11 % 43.22 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
442 BirdNet
This method makes use of Velodyne laser scans.
 57.12 % 79.30 % 55.16 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
443 ACF-SC 56.60 % 69.90 % 43.61 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
444 LSVM-MDPM-us code 55.95 % 68.94 % 41.45 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
445 ACF 54.09 % 63.05 % 41.81 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
446 Mono3D_PLiDAR code 53.36 % 80.85 % 44.80 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
447 Graph-NMS-baseline 52.92 % 76.21 % 43.38 % 47 ms GPU @ 2.5 Ghz (Python)
448 RT3D-GMP
This method uses stereo information.
 51.95 % 62.41 % 39.14 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
449 VeloFCN
This method makes use of Velodyne laser scans.
 51.82 % 70.53 % 45.70 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
450 Vote3D
This method makes use of Velodyne laser scans.
 45.94 % 54.38 % 40.48 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
451 TopNet-HighRes
This method makes use of Velodyne laser scans.
 45.85 % 58.04 % 41.11 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
452 RT3DStereo
This method uses stereo information.
 45.81 % 56.53 % 37.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
453 Multimodal Detection
This method makes use of Velodyne laser scans.
 code 45.46 % 63.91 % 37.25 % 0.06 s GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
454 CDTrack3D code 42.11 % 46.54 % 34.20 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
455 RT3D
This method makes use of Velodyne laser scans.
 39.69 % 50.33 % 40.04 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
456 R-AGNO-Net 36.55 % 49.87 % 35.20 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
457 VoxelJones code 36.31 % 43.89 % 34.16 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
458 CSoR
This method makes use of Velodyne laser scans.
 code 21.66 % 31.52 % 17.99 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
459 mBoW
This method makes use of Velodyne laser scans.
 21.59 % 35.22 % 16.89 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
460 DepthCN
This method makes use of Velodyne laser scans.
 code 21.18 % 37.45 % 16.08 % 2.3 s GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
461 YOLOv2 code 14.31 % 26.74 % 10.94 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
462 TopNet-UncEst
This method makes use of Velodyne laser scans.
 6.24 % 7.24 % 5.42 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
463 TopNet-Retina
This method makes use of Velodyne laser scans.
 5.00 % 6.82 % 4.52 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
464 test code 4.63 % 1.85 % 4.96 % 50 s 1 core @ 2.5 Ghz (Python)
465 TopNet-DecayRate
This method makes use of Velodyne laser scans.
 0.01 % 0.00 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
466 LaserNet 0.00 % 0.00 % 0.00 % 12 ms GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
467 Neighbor-Vote 0.00 % 0.00 % 0.00 % 0.1 s GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 DGIST MT-CNN 80.78 % 89.66 % 76.52 % 0.09 s GPU @ 1.0 Ghz (Python)
2 VueronNet 80.75 % 89.91 % 76.56 % 0.08 s GPU @ 2.5 Ghz (Python)
3 F-PointNet
This method makes use of Velodyne laser scans.
 code 80.13 % 89.83 % 75.05 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
4 NF2 79.59 % 88.28 % 75.47 % 0.1 s GPU @ 2.5 Ghz (Python)
5 TuSimple code 78.40 % 88.87 % 73.66 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
6 HHA-TFFEM
This method makes use of Velodyne laser scans.
 77.12 % 85.32 % 72.69 % 0.14 s GPU core @ 2.5 Ghz python
7 RRC code 76.61 % 85.98 % 71.47 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
8 WSSN
This method makes use of Velodyne laser scans.
 76.42 % 84.91 % 71.86 % 0.37 s GPU @ >3.5 Ghz (Python + C/C++)
Z. Guo, W. Liao, Y. Xiao, P. Veelaert and W. Philips: Weak Segmentation Supervised Deep Neural Networks for Pedestrian Detection. Pattern Recognition 2021.
9 ECP Faster R-CNN 76.25 % 85.96 % 70.55 % 0.25 s GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
10 Aston-EAS 76.07 % 86.71 % 70.02 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
11 MHN 75.99 % 87.21 % 69.50 % 0.39 s GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.
12 FFNet code 75.81 % 87.17 % 69.86 % 1.07 s GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
13 SJTU-HW 75.81 % 87.17 % 69.86 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
14 MS-CNN code 74.89 % 85.71 % 68.99 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
15 DD3D code 73.09 % 85.71 % 68.54 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
16 F-ConvNet
This method makes use of Velodyne laser scans.
 code 72.91 % 83.63 % 67.18 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
17 GN 72.29 % 82.93 % 65.56 % 1 s GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
18 SubCNN 72.27 % 84.88 % 66.82 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
19 VMVS
This method makes use of Velodyne laser scans.
 71.82 % 82.80 % 66.85 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
20 IVA code 71.37 % 84.61 % 64.90 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
21 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 70.76 % 83.79 % 64.81 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
22 SDP+RPN 70.42 % 82.07 % 65.09 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
23 3DOP
This method uses stereo information.
 code 69.57 % 83.17 % 63.48 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
24 MonoPSR code 68.56 % 85.60 % 63.34 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
25 DeepStereoOP 68.46 % 83.00 % 63.35 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
26 sensekitti code 68.41 % 82.72 % 62.72 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
27 Frustum-PointPillars code 67.51 % 76.80 % 63.81 % 0.06 s 4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
28 GFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 67.39 % 81.79 % 62.24 % 0.07 s GPU @ 2.5 Ghz (Python)
29 FII-CenterNet 67.31 % 81.32 % 61.29 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
30 Mono3D code 67.29 % 80.30 % 62.23 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
31 SFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 67.17 % 82.48 % 62.02 % 0.04 s GPU @ 2.5 Ghz (Python)
32 CAD 66.40 % 76.10 % 63.58 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
33 Faster R-CNN code 66.24 % 79.97 % 61.09 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
34 VPFNet code 65.68 % 75.03 % 61.95 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
35 EQ-PVRCNN 65.01 % 77.19 % 61.95 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
36 MM-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 64.91 % 79.88 % 59.60 % 0.04 s GPU @ 2.5 Ghz (Python)
37 SDP+CRC (ft) 64.36 % 79.22 % 59.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
38 Pose-RCNN 63.54 % 80.07 % 57.02 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
39 PV-RCNN++ 63.47 % 72.82 % 60.96 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
40 ADLAB 63.25 % 70.86 % 60.52 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
41 CFM 62.84 % 74.76 % 56.06 % <2 s GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
42 CasA 62.73 % 72.65 % 60.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
43 PiFeNet 62.68 % 71.97 % 59.77 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
44 Fast-CLOCs 62.57 % 76.22 % 60.13 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
45 KAIST-VDCLab 62.35 % 79.37 % 57.42 % 0.04 s 1 core @ 2.5 Ghz (Python)
46 HotSpotNet 62.31 % 71.43 % 59.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
47 VCT 62.00 % 71.19 % 58.52 % 0.2 s 1 core @ 2.5 Ghz (Python)
48 P2V-RCNN 61.83 % 71.76 % 59.29 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
49 TBD 61.74 % 70.58 % 59.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
50 PE-RCVN 61.64 % 69.49 % 59.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
51 MonoPair 61.57 % 78.81 % 56.51 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
52 H^23D R-CNN 61.50 % 72.21 % 57.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
53 SAA-PV-RCNN 61.41 % 70.35 % 58.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
54 monodle code 61.29 % 78.66 % 56.18 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
55 RPN+BF code 61.22 % 77.06 % 55.22 % 0.6 s GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
56 EA-M-RCNN(BorderAtt) 61.06 % 73.07 % 56.86 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
57 ISE-RCNN-PV 61.06 % 70.59 % 57.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
58 Regionlets 60.83 % 73.79 % 54.72 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
59 GA-Aug 60.78 % 76.78 % 55.00 % 0.04 s GPU @ 2.5 Ghz (Python)
60 ISE-RCNN 60.70 % 69.41 % 58.49 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
61 3DSSD code 60.51 % 72.33 % 56.28 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
62 TBD 60.30 % 70.50 % 57.06 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
63 FS-Net
This method makes use of Velodyne laser scans.
 60.13 % 69.08 % 57.91 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
64 USVLab BSAODet (SM) 60.08 % 70.93 % 57.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
65 USVLab BSAODet (MM) 59.83 % 69.64 % 57.31 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
66 Generalized-SIENet 59.54 % 69.16 % 57.33 % 0.08 s 1 core @ 2.5 Ghz (Python)
67 VPN 59.48 % 70.97 % 55.29 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
68 AutoAlign 59.48 % 70.17 % 55.37 % 0.1 s 1 core @ 2.5 Ghz (Python)
69 Fast VP-RCNN code 59.32 % 69.51 % 56.66 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
70 QD-3DT
This is an online method (no batch processing).
 code 59.26 % 78.41 % 54.37 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
71 TANet code 59.07 % 69.90 % 56.44 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
72 SARFE 59.07 % 68.25 % 56.74 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
73 anonymous code 59.04 % 69.62 % 56.45 % 0.05s 1 core @ >3.5 Ghz (python)
74 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 58.81 % 66.93 % 56.57 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
75 SRDL 58.70 % 68.45 % 56.23 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
76 FusionDetv1 58.68 % 68.44 % 56.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
77 P2V_PCV1 58.59 % 68.62 % 56.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
78 EG_DETR 58.58 % 74.53 % 53.76 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
79 SA-voxel-centernet code 58.50 % 66.89 % 56.25 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
80 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 58.37 % 68.88 % 55.38 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
81 SCIR-Net
This method makes use of Velodyne laser scans.
 58.37 % 68.18 % 55.78 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
82 Point-GNN
This method makes use of Velodyne laser scans.
 code 58.20 % 71.59 % 54.06 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
83 TPCG 58.17 % 67.39 % 55.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 DeepParts 58.15 % 71.47 % 51.92 % ~1 s GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
85 CompACT-Deep 58.14 % 70.93 % 52.29 % 1 s 1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
86 EPNet++ 58.10 % 68.58 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
87 DSGN++
This method uses stereo information.
 58.09 % 69.70 % 54.45 % 0.4 s NVIDIA Tesla V100
88 WHUT-iou_ssd code 58.03 % 66.60 % 55.82 % 0.045s 1 core @ 2.5 Ghz (C/C++)
89 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 57.96 % 68.78 % 54.01 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
90 SVGA-Net 57.92 % 67.81 % 55.25 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
91 Point Image Fusion 57.91 % 66.47 % 55.48 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
92 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 57.87 % 67.95 % 55.23 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
93 TBD
This method makes use of Velodyne laser scans.
 57.81 % 67.54 % 54.30 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
94 sa-voxel-centernet code 57.79 % 66.03 % 55.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
95 Lite-FPN-GUPNet 57.56 % 76.82 % 50.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
96 LazyTorch-CP-Infer-O 57.47 % 67.75 % 55.08 % 1 s 1 core @ 2.5 Ghz (C/C++)
97 FPC-RCNN 57.46 % 66.88 % 55.09 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
98 LazyTorch-CP 57.38 % 67.60 % 54.98 % 1 s 1 core @ 2.5 Ghz (C/C++)
99 SGNet 57.36 % 65.93 % 53.82 % 0.09 s GPU @ 2.5 Ghz (Python)
100 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 57.35 % 67.88 % 54.42 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
101 LazyTorch-CP-Small-P 57.35 % 67.67 % 55.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
102 PDV 57.34 % 65.94 % 54.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
103 MVOD 57.33 % 66.72 % 54.31 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
104 SIF 57.32 % 67.78 % 54.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
105 FromVoxelToPoint code 57.26 % 68.26 % 54.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
106 SemanticVoxels 57.22 % 67.62 % 54.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
107 FPCR-CNN 57.14 % 66.59 % 54.43 % 0.05 s 1 core @ 2.5 Ghz (Python)
108 DGT-Det3D 57.04 % 66.94 % 54.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
109 FusionDetv2-v3 56.93 % 65.96 % 54.73 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
110 IA-SSD (single) 56.87 % 66.69 % 54.68 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
111 DDet 56.81 % 65.02 % 54.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
112 CAT-Det 56.75 % 67.15 % 53.44 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
113 FRCNN+Or code 56.68 % 71.64 % 51.53 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
114 City-CF-fixed 56.62 % 67.73 % 53.85 % 1 s 1 core @ 2.5 Ghz (C/C++)
115 FusionDetv2-v4 56.60 % 65.80 % 54.55 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
116 FilteredICF 56.53 % 69.79 % 50.32 % ~ 2 s >8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
117 ARPNET 56.42 % 69.08 % 52.69 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
118 TBD 56.42 % 65.60 % 53.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
119 TBD 56.42 % 65.60 % 53.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
120 FusionDetv2-v5 56.40 % 65.17 % 53.89 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
121 MonoRUn code 56.40 % 73.05 % 51.40 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
122 MV-RGBD-RF
This method makes use of Velodyne laser scans.
 56.18 % 72.99 % 49.72 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
123 TBD_IOU1 56.02 % 66.44 % 53.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
124 TBD 55.96 % 66.20 % 53.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
125 SAA-SECOND 55.95 % 65.50 % 52.88 % 38m s 1 core @ 2.5 Ghz (C/C++)
126 TBD_IOU 55.90 % 64.94 % 53.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
127 MGAF-3DSSD code 55.80 % 66.31 % 52.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
128 VCRCNN 55.66 % 64.59 % 53.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 GCDR 55.65 % 74.95 % 48.44 % 0.28 s 1 core @ 2.5 Ghz (Python)
130 GUPNet code 55.65 % 74.95 % 48.44 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
131 MLOD
This method makes use of Velodyne laser scans.
 code 55.62 % 68.42 % 51.45 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
132 Dune-DCF-e11 55.62 % 66.39 % 53.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
133 gupnet_se 55.50 % 74.80 % 50.57 % 0.03s 1 core @ 2.5 Ghz (C/C++)
134 ST-RCNN
This method makes use of Velodyne laser scans.
 55.46 % 64.50 % 52.89 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
135 FusionDetv2-v2 55.32 % 62.74 % 52.43 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
136 MSADet 55.31 % 66.73 % 52.97 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
137 TCDVF 55.18 % 65.12 % 52.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
138 TBD 55.14 % 63.44 % 53.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
139 PointPillars
This method makes use of Velodyne laser scans.
 code 55.10 % 65.29 % 52.39 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
140 Dune-DCF-e15 55.09 % 65.54 % 52.36 % 1 s 1 core @ 2.5 Ghz (C/C++)
141 FPC3D_all
This method makes use of Velodyne laser scans.
 55.08 % 64.74 % 52.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
142 ANM 55.08 % 74.09 % 48.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
143 CrazyTensor-CP 55.05 % 64.92 % 52.88 % 1 s 1 core @ 2.5 Ghz (Python)
144 STD code 55.04 % 68.33 % 50.85 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
145 Dune-DCF-e09 55.01 % 65.56 % 52.43 % 1 s 1 core @ 2.5 Ghz (C/C++)
146 NV-RCNN 54.98 % 64.78 % 51.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
147 GAC3D++ 54.96 % 74.08 % 50.06 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
148 Vote3Deep
This method makes use of Velodyne laser scans.
 54.80 % 67.99 % 51.17 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
149 City-CF 54.79 % 65.01 % 52.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
150 M3DeTR code 54.78 % 63.15 % 52.49 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
151 DSASNet 54.67 % 64.59 % 51.05 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
152 tbd 54.44 % 64.96 % 50.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
153 epBRM
This method makes use of Velodyne laser scans.
 code 54.13 % 62.90 % 51.95 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
154 DVFENet 54.13 % 63.54 % 51.79 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
155 OSE+ 54.12 % 68.48 % 49.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
156 XView 53.83 % 62.27 % 51.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
157 PointPainting
This method makes use of Velodyne laser scans.
 53.76 % 61.86 % 50.61 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
158 PDV2 53.54 % 65.59 % 47.65 % 3.7 s 1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
159 Mix-Teaching-M3D 53.52 % 67.34 % 47.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
160 TBD 53.34 % 64.17 % 51.00 % TBD GPU @ 2.5 Ghz (Python + C/C++)
161 ASCNet 53.28 % 62.40 % 50.88 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
162 TAFT 53.15 % 67.62 % 47.08 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
163 FPV-SSD 53.10 % 62.00 % 50.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
164 Disp R-CNN
This method uses stereo information.
 code 52.98 % 71.79 % 48.20 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
165 FusionDetv2-baseline 52.96 % 58.96 % 50.94 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
166 Disp R-CNN (velo)
This method uses stereo information.
 code 52.90 % 71.63 % 48.15 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
167 demo 52.90 % 63.79 % 49.54 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
168 pAUCEnsT 52.88 % 65.84 % 46.97 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
169 SparVox3D 52.84 % 69.33 % 48.49 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
170 PFF3D
This method makes use of Velodyne laser scans.
 code 52.53 % 62.12 % 50.27 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
171 NV2P-RCNN 52.52 % 61.80 % 50.24 % 0.1 s GPU @ 2.5 Ghz (Python)
172 HS3D code 52.50 % 63.73 % 49.78 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
173 MDNet 52.46 % 68.56 % 47.69 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
174 IA-SSD (multi) 52.45 % 65.07 % 50.20 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
175 S-AT GCN 52.30 % 62.01 % 50.10 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
176 MMLAB LIGA-Stereo
This method uses stereo information.
 code 52.18 % 65.59 % 49.29 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
177 SCSTSV-MonoFlex 52.18 % 67.51 % 45.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
178 CrazyTensor-CF 52.00 % 62.19 % 49.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
179 YF 51.82 % 61.37 % 48.59 % 0.04 s GPU @ 2.5 Ghz (C/C++)
180 Shift R-CNN (mono) code 51.30 % 70.86 % 46.37 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
181 TBD_BD code 50.60 % 60.91 % 48.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
182 TBD 50.45 % 60.47 % 47.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
183 MonoGround 50.35 % 63.96 % 45.75 % 0.03 s 1 core @ 2.5 Ghz (Python)
184 SCNet
This method makes use of Velodyne laser scans.
 49.61 % 60.95 % 46.91 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
185 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 49.41 % 58.93 % 46.44 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
186 Y4 code 49.24 % 68.07 % 44.42 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
187 vadin-TBD 48.97 % 63.77 % 44.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
188 MonoFlex 48.64 % 61.96 % 44.17 % 0.03 s 1 core @ 2.5 Ghz (Python)
189 ACFD
This method makes use of Velodyne laser scans.
 code 48.63 % 61.62 % 44.15 % 0.2 s 4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
190 R-CNN 48.57 % 62.88 % 43.05 % 4 s GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
191 MonoEdge 48.26 % 61.11 % 42.37 % 0.05 s GPU @ 2.5 Ghz (Python)
192 MK3D 48.24 % 67.43 % 41.12 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
193 RelationNet3D_dla34 code 47.93 % 63.34 % 43.42 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
194 LIGA-Stereo-old
This method uses stereo information.
 47.75 % 58.61 % 44.39 % 0.375 s Titan Xp
195 MonoFlex 47.58 % 62.64 % 43.15 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
196 MonoEdge-Rotate 47.56 % 62.79 % 43.02 % 0.05 s GPU @ 2.5 Ghz (Python)
197 BirdNet+
This method makes use of Velodyne laser scans.
 code 47.50 % 54.78 % 45.53 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
198 Contrastive PP code 47.05 % 56.47 % 45.04 % 0.01 s 1 core @ 2.5 Ghz (Python)
199 mono3d 46.73 % 58.34 % 41.92 % 0.03 s GPU @ 2.5 Ghz (Python)
200 PP-PCdet code 45.82 % 54.17 % 43.68 % 0.01 s 1 core @ 2.5 Ghz (Python)
201 SS3D 45.79 % 61.58 % 41.14 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
202 ACF 45.67 % 59.81 % 40.88 % 1 s 1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
203 Fusion-DPM
This method makes use of Velodyne laser scans.
 code 44.99 % 58.93 % 40.19 % ~ 30 s 1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
204 M3DSSD++ code 44.89 % 58.54 % 40.66 % 0.16s 1 core @ 2.5 Ghz (C/C++)
205 ACF-MR 44.79 % 58.29 % 39.94 % 0.6 s 1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
206 MonoGeo 44.63 % 58.49 % 40.41 % 0.05 s 1 core @ 2.5 Ghz (Python)
207 Geo3D 44.42 % 58.74 % 40.13 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
208 LPCG-Monoflex 44.13 % 62.44 % 39.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
209 SAIC_ADC_Mono3D code 43.96 % 59.27 % 39.83 % 50 s GPU @ 2.5 Ghz (Python)
210 HA-SSVM 43.87 % 58.76 % 38.81 % 21 s 1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
211 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 43.86 % 54.55 % 40.99 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
212 MonoEF code 43.73 % 58.79 % 39.45 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
213 MonoLCD 43.71 % 57.69 % 39.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
214 City-ICN 43.56 % 60.56 % 38.71 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
215 D4LCN code 43.50 % 59.55 % 37.12 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
216 DisposalNet
This method uses stereo information.
 43.43 % 52.99 % 41.29 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
217 CrazyTensor-ICN 43.43 % 60.32 % 38.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
218 MonoDDE 43.36 % 57.80 % 39.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
219 DPM-VOC+VP 43.26 % 59.21 % 38.12 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
220 ACF-SC 42.97 % 53.30 % 38.12 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
221 MonoDTR 42.86 % 59.44 % 38.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
222 LT-M3OD 42.72 % 57.14 % 38.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
223 SquaresICF code 42.61 % 57.08 % 37.85 % 1 s GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
224 CG-Stereo
This method uses stereo information.
 42.54 % 54.64 % 38.45 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
225 MonoCon code 42.44 % 56.57 % 36.34 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
226 RelationNet3D_res18 code 42.25 % 57.61 % 37.93 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
227 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 41.97 % 51.38 % 40.15 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
228 ICCV 41.88 % 55.14 % 37.55 % 0.04 s GPU @ 2.5 Ghz (Python)
229 DDMP-3D 41.54 % 56.73 % 35.52 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
230 CSW3D
This method makes use of Velodyne laser scans.
 41.50 % 53.76 % 37.25 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
231 M3D-RPN code 41.46 % 56.64 % 37.31 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
232 YOLOStereo3D
This method uses stereo information.
 code 41.46 % 56.20 % 37.07 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
233 MP-Mono 40.87 % 55.79 % 36.81 % 0.16 s GPU @ 2.5 Ghz (Python)
234 SwinMono3D 40.54 % 56.73 % 36.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
235 SubCat 40.50 % 53.75 % 35.66 % 1.2 s 6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
236 PS-fld 40.47 % 55.47 % 36.65 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
237 DSGN
This method uses stereo information.
 code 39.93 % 49.28 % 38.13 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
238 RT3D-GMP
This method uses stereo information.
 39.83 % 55.56 % 35.18 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
239 FusionDetv2-v1 39.60 % 46.77 % 38.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
240 SparsePool code 39.59 % 50.81 % 35.91 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
241 Graph-NMS-baseline 39.46 % 52.32 % 35.60 % 47 ms GPU @ 2.5 Ghz (Python)
242 SparsePool code 39.43 % 50.94 % 35.78 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
243 AVOD
This method makes use of Velodyne laser scans.
 code 39.43 % 50.90 % 35.75 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
244 ACF 39.12 % 48.42 % 35.03 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
245 Graph-NMS 38.40 % 51.24 % 35.24 % 36 ms GPU @ 2.5 Ghz (Python)
246 LSVM-MDPM-sv 37.26 % 50.74 % 33.13 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
247 multi-task CNN 37.00 % 49.38 % 33.46 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
248 Lite-FPN 36.93 % 50.63 % 32.88 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
249 Complexer-YOLO
This method makes use of Velodyne laser scans.
 36.45 % 42.16 % 32.91 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
250 LSVM-MDPM-us code 35.92 % 48.73 % 31.70 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
251 mono3d code 35.86 % 48.61 % 32.10 % TBD TBD
252 COF3D 35.34 % 50.45 % 31.28 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
253 Aug3D-RPN 34.95 % 47.22 % 30.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
254 MonoHMOO 34.74 % 49.26 % 30.37 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
255 PPTrans 34.45 % 45.02 % 30.94 % 0.2 s GPU @ 2.5 Ghz (Python)
256 CMKD 34.41 % 47.09 % 30.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
257 PointRGBNet 33.92 % 44.35 % 30.43 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
258 PGD-FCOS3D code 33.67 % 48.30 % 29.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
259 ZongmuMono3d code 33.47 % 45.86 % 29.84 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
260 Vote3D
This method makes use of Velodyne laser scans.
 33.04 % 42.66 % 30.59 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
261 EGFN
This method uses stereo information.
 32.60 % 44.09 % 29.10 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
262 SGM3D 32.48 % 45.03 % 28.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
263 CaDDN code 32.42 % 46.35 % 29.98 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
264 HBD 31.99 % 44.66 % 28.21 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
265 DFR-Net 31.84 % 45.20 % 27.94 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
266 K3D 31.71 % 44.40 % 27.90 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
267 OC Stereo
This method uses stereo information.
 code 30.79 % 43.50 % 28.40 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
268 mBoW
This method makes use of Velodyne laser scans.
 30.26 % 41.52 % 26.34 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
269 BirdNet
This method makes use of Velodyne laser scans.
 30.07 % 36.82 % 28.40 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
270 CDTrack3D code 29.98 % 42.95 % 27.59 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
271 SOD 29.47 % 46.61 % 26.97 % 0.1 s 1 core @ 2.5 Ghz (Python)
272 RT3DStereo
This method uses stereo information.
 29.30 % 41.12 % 25.25 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
273 AEC3D 28.32 % 37.26 % 24.98 % 18 ms GPU @ 2.5 Ghz (Python)
274 BEVC 26.52 % 35.31 % 24.72 % 35ms GPU @ 1.5 Ghz (Python)
275 DPM-C8B1
This method uses stereo information.
 25.34 % 36.40 % 22.00 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
276 MM 25.20 % 35.72 % 21.90 % 1 s 1 core @ 2.5 Ghz (C/C++)
277 VN3D 23.61 % 30.51 % 21.97 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
278 MAOLoss code 23.60 % 32.07 % 21.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
279 RefinedMPL 20.81 % 30.41 % 18.72 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
280 E2E-DA 19.96 % 29.93 % 17.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
281 E2E-DA-Lite (Res18) 19.87 % 32.25 % 17.37 % 0.01 s GPU @ 2.5 Ghz (Python)
282 TopNet-Retina
This method makes use of Velodyne laser scans.
 16.45 % 22.37 % 15.43 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
283 TopNet-HighRes
This method makes use of Velodyne laser scans.
 15.28 % 21.22 % 13.89 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
284 YOLOv2 code 11.46 % 15.37 % 9.67 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
285 TopNet-UncEst
This method makes use of Velodyne laser scans.
 8.58 % 13.00 % 7.38 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
286 BIP-HETERO 7.05 % 8.51 % 6.30 % ~2 s 1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
287 EW code 1.71 % 1.74 % 1.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
288 EM code 1.70 % 1.67 % 1.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
289 TopNet-DecayRate
This method makes use of Velodyne laser scans.
 0.01 % 0.01 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 CasA 83.21 % 92.86 % 77.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 ISE-RCNN-PV 82.78 % 88.08 % 75.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
3 SARFE 82.00 % 90.01 % 75.30 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
4 SGNet 82.00 % 91.55 % 75.30 % 0.09 s GPU @ 2.5 Ghz (Python)
5 ISE-RCNN 81.95 % 87.68 % 75.17 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
6 NF2 81.75 % 89.67 % 71.43 % 0.1 s GPU @ 2.5 Ghz (Python)
7 RangeIoUDet
This method makes use of Velodyne laser scans.
 81.67 % 90.43 % 74.90 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
8 anonymous code 81.52 % 89.31 % 74.71 % 0.05s 1 core @ >3.5 Ghz (python)
9 Fast VP-RCNN code 81.41 % 89.29 % 74.88 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
10 Point Image Fusion 80.73 % 87.99 % 74.32 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
11 TPCG 80.71 % 87.73 % 74.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 SAA-PV-RCNN 80.71 % 88.94 % 73.79 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
13 CAT-Det 80.70 % 87.94 % 73.86 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
14 SPG_mini
This method makes use of Velodyne laser scans.
 code 80.58 % 87.77 % 74.86 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
15 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 80.57 % 88.65 % 74.81 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
16 FS-Net
This method makes use of Velodyne laser scans.
 80.54 % 87.05 % 73.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
17 CAD 80.53 % 91.65 % 73.56 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
18 BtcDet
This method makes use of Velodyne laser scans.
 code 80.46 % 88.41 % 74.59 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
19 VCRCNN 80.46 % 87.34 % 73.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
20 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 80.42 % 86.62 % 73.64 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
21 EQ-PVRCNN 80.37 % 89.07 % 74.20 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
22 Generalized-SIENet 80.07 % 89.23 % 73.76 % 0.08 s 1 core @ 2.5 Ghz (Python)
23 USVLab BSAODet (MM) 80.04 % 88.51 % 73.38 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
24 WHUT-iou_ssd code 79.98 % 87.31 % 74.30 % 0.045s 1 core @ 2.5 Ghz (C/C++)
25 sa-voxel-centernet code 79.98 % 88.08 % 73.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
26 SA-voxel-centernet code 79.92 % 86.43 % 73.47 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
27 FPC-RCNN 79.89 % 88.62 % 73.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
28 PDV 79.84 % 88.76 % 73.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
29 DSASNet 79.56 % 87.41 % 73.08 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
30 DDet 79.47 % 88.65 % 72.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
31 USVLab BSAODet (SM) 79.34 % 89.04 % 72.50 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
32 M3DeTR code 79.29 % 87.38 % 72.46 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
33 PV-RCNN++ 79.22 % 85.76 % 72.35 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
34 TBD 78.83 % 84.96 % 72.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
35 HotSpotNet 78.81 % 86.06 % 71.74 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
36 JPVNet 78.73 % 87.42 % 72.45 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
37 IA-SSD (single) 78.71 % 88.99 % 72.03 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
38 PE-RCVN 78.71 % 90.77 % 71.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
39 TBD
This method makes use of Velodyne laser scans.
 78.59 % 86.51 % 73.55 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
40 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 78.29 % 88.90 % 71.19 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
41 F-ConvNet
This method makes use of Velodyne laser scans.
 code 78.05 % 86.75 % 68.12 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
42 PointPainting
This method makes use of Velodyne laser scans.
 78.04 % 87.70 % 69.27 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
43 AutoAlign 77.96 % 89.63 % 71.27 % 0.1 s 1 core @ 2.5 Ghz (Python)
44 MVOD 77.92 % 85.92 % 71.24 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
45 TBD 77.82 % 86.21 % 71.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
46 TCDVF 77.66 % 86.38 % 71.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 ST-RCNN
This method makes use of Velodyne laser scans.
 77.57 % 85.84 % 70.92 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
48 SAA-SECOND 77.47 % 88.50 % 70.23 % 38m s 1 core @ 2.5 Ghz (C/C++)
49 TBD 77.06 % 90.28 % 70.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
50 ASCNet 76.95 % 83.81 % 70.84 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
51 P2V-RCNN 76.93 % 88.40 % 70.35 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
52 RRC code 76.81 % 86.81 % 66.59 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
53 DGT-Det3D 76.71 % 86.58 % 70.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
54 FusionDetv2-v5 76.60 % 85.95 % 69.75 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
55 FusionDetv2-v3 76.48 % 86.15 % 69.64 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
56 NV2P-RCNN 76.29 % 83.53 % 69.57 % 0.1 s GPU @ 2.5 Ghz (Python)
57 VCT 76.08 % 86.69 % 69.91 % 0.2 s 1 core @ 2.5 Ghz (Python)
58 FusionDetv2-v4 75.92 % 87.66 % 70.06 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
59 FPCR-CNN 75.83 % 87.74 % 68.98 % 0.05 s 1 core @ 2.5 Ghz (Python)
60 FPV-SSD 75.33 % 83.30 % 68.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
61 MS-CNN code 75.30 % 84.88 % 65.27 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
62 TuSimple code 75.22 % 83.68 % 65.22 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
63 SVGA-Net 75.14 % 85.13 % 68.14 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
64 Point-GNN
This method makes use of Velodyne laser scans.
 code 75.08 % 85.75 % 68.69 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
65 Fast-CLOCs 75.07 % 89.73 % 67.93 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
66 SCIR-Net
This method makes use of Velodyne laser scans.
 74.88 % 84.92 % 68.13 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
67 EA-M-RCNN(BorderAtt) 74.85 % 88.69 % 68.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
68 Deep3DBox 74.78 % 84.36 % 64.05 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
69 DGIST MT-CNN 74.77 % 86.95 % 66.05 % 0.09 s GPU @ 1.0 Ghz (Python)
70 P2V_PCV1 74.57 % 85.38 % 68.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 VPFNet code 74.52 % 82.60 % 66.04 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
72 FusionDetv2-v2 74.38 % 86.62 % 67.96 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
73 3DSSD code 74.12 % 87.09 % 67.67 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
74 TBD 74.06 % 84.24 % 68.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 MSADet 74.06 % 87.54 % 68.55 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
76 VPN 73.99 % 89.56 % 66.86 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
77 SDP+RPN 73.85 % 82.59 % 64.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
78 TBD_IOU 73.84 % 87.97 % 66.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 FusionDetv1 73.69 % 85.39 % 66.94 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
80 SRDL 73.68 % 85.44 % 66.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
81 DVFENet 73.66 % 85.45 % 67.10 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
82 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 73.63 % 85.43 % 66.64 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
83 sensekitti code 73.48 % 82.90 % 64.03 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
84 TBD_IOU1 73.46 % 87.05 % 66.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 73.42 % 86.21 % 66.45 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
86 FPC3D_all
This method makes use of Velodyne laser scans.
 73.25 % 84.12 % 66.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
87 SIF 73.19 % 85.18 % 65.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
88 F-PointNet
This method makes use of Velodyne laser scans.
 code 73.16 % 86.86 % 65.21 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
89 FromVoxelToPoint code 73.16 % 87.07 % 65.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
90 XView 73.16 % 88.02 % 65.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
91 NV-RCNN 73.07 % 85.64 % 66.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 HS3D code 73.02 % 84.59 % 67.13 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
93 demo 72.90 % 87.20 % 66.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
94 City-CF-fixed 72.86 % 86.69 % 66.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
95 S-AT GCN 72.81 % 82.79 % 66.72 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
96 H^23D R-CNN code 72.73 % 85.50 % 65.81 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
97 City-CF 72.68 % 86.44 % 66.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
98 FusionDetv2-baseline 72.68 % 82.09 % 66.42 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
99 Dune-DCF-e15 72.38 % 86.51 % 66.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
100 MonoPSR code 72.08 % 82.06 % 62.43 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
101 ARPNET 71.95 % 84.96 % 65.21 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
102 Dune-DCF-e11 71.82 % 86.21 % 65.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
103 SubCNN 71.72 % 79.36 % 62.74 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
104 STD code 71.63 % 83.99 % 64.92 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
105 LazyTorch-CP-Infer-O 70.65 % 83.62 % 64.20 % 1 s 1 core @ 2.5 Ghz (C/C++)
106 LazyTorch-CP 70.54 % 83.36 % 64.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
107 LazyTorch-CP-Small-P 70.48 % 83.46 % 64.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
108 IA-SSD (multi) 70.46 % 84.98 % 65.55 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
109 MGAF-3DSSD code 70.41 % 86.42 % 63.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
110 Dune-DCF-e09 70.28 % 83.14 % 64.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
111 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 70.18 % 82.86 % 63.55 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
112 TBD 70.12 % 81.15 % 63.79 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
113 YF 70.10 % 80.50 % 64.21 % 0.04 s GPU @ 2.5 Ghz (C/C++)
114 TBD 69.99 % 85.54 % 65.02 % TBD GPU @ 2.5 Ghz (Python + C/C++)
115 TBD_BD code 69.56 % 83.36 % 63.39 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
116 CrazyTensor-CP 69.31 % 83.67 % 62.97 % 1 s 1 core @ 2.5 Ghz (Python)
117 CrazyTensor-CF 69.23 % 85.28 % 62.37 % 1 s 1 core @ 2.5 Ghz (C/C++)
118 PointPillars
This method makes use of Velodyne laser scans.
 code 68.98 % 83.97 % 62.17 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
119 Vote3Deep
This method makes use of Velodyne laser scans.
 68.82 % 78.41 % 62.50 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
120 3DOP
This method uses stereo information.
 code 68.71 % 80.52 % 61.07 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
121 Pose-RCNN 68.40 % 81.53 % 59.43 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
122 EPNet++ 68.30 % 80.27 % 63.00 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
123 TANet code 68.20 % 82.24 % 62.13 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
124 Contrastive PP code 67.62 % 79.63 % 60.81 % 0.01 s 1 core @ 2.5 Ghz (Python)
125 IVA code 67.57 % 78.48 % 58.83 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
126 DeepStereoOP 67.22 % 79.35 % 58.60 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
127 TBD 67.15 % 82.44 % 60.87 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
128 TBD 67.15 % 82.44 % 60.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
129 VueronNet 66.92 % 82.71 % 59.01 % 0.08 s GPU @ 2.5 Ghz (Python)
130 PP-PCdet code 66.58 % 78.44 % 60.22 % 0.01 s 1 core @ 2.5 Ghz (Python)
131 FII-CenterNet 66.54 % 79.04 % 57.76 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
132 epBRM
This method makes use of Velodyne laser scans.
 code 66.51 % 79.65 % 60.31 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
133 PFF3D
This method makes use of Velodyne laser scans.
 code 66.25 % 79.44 % 60.11 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
134 DD3D code 65.98 % 81.13 % 58.86 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
135 PointRGBNet 65.98 % 79.87 % 59.75 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
136 BirdNet+
This method makes use of Velodyne laser scans.
 code 65.40 % 72.96 % 60.23 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
137 Mono3D code 65.15 % 77.19 % 57.88 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
138 PiFeNet 64.39 % 80.02 % 57.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
139 tbd 64.31 % 79.83 % 58.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
140 DisposalNet
This method uses stereo information.
 63.39 % 74.69 % 56.96 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
141 Faster R-CNN code 62.86 % 72.40 % 54.97 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
142 SCNet
This method makes use of Velodyne laser scans.
 62.50 % 78.48 % 56.34 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
143 DSGN++
This method uses stereo information.
 62.10 % 77.71 % 55.78 % 0.4 s NVIDIA Tesla V100
144 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 60.79 % 70.38 % 55.37 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
145 SDP+CRC (ft) 60.72 % 75.63 % 53.00 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
146 Complexer-YOLO
This method makes use of Velodyne laser scans.
 59.78 % 66.94 % 55.63 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
147 Mix-Teaching-M3D 58.65 % 75.15 % 50.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
148 Regionlets 58.52 % 71.12 % 50.83 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
149 SFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 57.71 % 76.35 % 50.56 % 0.04 s GPU @ 2.5 Ghz (Python)
150 FRCNN+Or code 57.01 % 70.99 % 50.14 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
151 GFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 56.93 % 74.59 % 49.91 % 0.07 s GPU @ 2.5 Ghz (Python)
152 QD-3DT
This is an online method (no batch processing).
 code 56.51 % 75.55 % 49.70 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
153 MonoPair 56.37 % 74.77 % 48.37 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
154 FusionDetv2-v1 56.30 % 66.22 % 52.29 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
155 MLOD
This method makes use of Velodyne laser scans.
 code 56.04 % 75.35 % 49.11 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
156 LIGA-Stereo-old
This method uses stereo information.
 55.77 % 74.25 % 49.68 % 0.375 s Titan Xp
157 MDNet 55.58 % 69.87 % 46.54 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
158 MM-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
 54.90 % 69.95 % 48.49 % 0.04 s GPU @ 2.5 Ghz (Python)
159 mono3d 54.82 % 70.77 % 47.55 % 0.03 s GPU @ 2.5 Ghz (Python)
160 MonoFlex 54.76 % 72.41 % 46.21 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
161 M3DSSD++ code 54.62 % 69.35 % 46.25 % 0.16s 1 core @ 2.5 Ghz (C/C++)
162 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 54.61 % 74.97 % 50.29 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
163 MMLAB LIGA-Stereo
This method uses stereo information.
 code 54.57 % 74.40 % 48.11 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
164 SCSTSV-MonoFlex 54.42 % 75.37 % 46.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
165 vadin-TBD 54.12 % 70.14 % 46.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
166 monodle code 53.29 % 70.78 % 45.01 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
167 LPCG-Monoflex 53.04 % 72.36 % 46.11 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
168 GA-Aug 52.71 % 67.62 % 46.37 % 0.04 s GPU @ 2.5 Ghz (Python)
169 AVOD
This method makes use of Velodyne laser scans.
 code 52.60 % 66.45 % 46.39 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
170 MonoEdge-Rotate 51.89 % 68.91 % 45.33 % 0.05 s GPU @ 2.5 Ghz (Python)
171 MonoGround 51.67 % 67.78 % 45.11 % 0.03 s 1 core @ 2.5 Ghz (Python)
172 Geo3D 51.26 % 71.75 % 44.44 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
173 MonoFlex 51.23 % 66.73 % 44.57 % 0.03 s 1 core @ 2.5 Ghz (Python)
174 MonoDDE 51.10 % 70.85 % 44.02 % 0.04 s 1 core @ 2.5 Ghz (Python)
175 MonoGeo 50.48 % 65.42 % 42.48 % 0.05 s 1 core @ 2.5 Ghz (Python)
176 MonoDTR 49.48 % 64.93 % 42.76 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
177 MonoEdge 49.33 % 65.93 % 42.44 % 0.05 s GPU @ 2.5 Ghz (Python)
178 MonoRUn code 49.13 % 67.47 % 43.41 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
179 MP-Mono 48.54 % 69.48 % 41.58 % 0.16 s GPU @ 2.5 Ghz (Python)
180 CG-Stereo
This method uses stereo information.
 48.46 % 69.98 % 42.41 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
181 EG_DETR 48.42 % 67.71 % 42.99 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
182 BirdNet
This method makes use of Velodyne laser scans.
 47.64 % 64.91 % 44.59 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
183 CMKD 47.21 % 66.52 % 41.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
184 KAIST-VDCLab 46.65 % 68.60 % 41.79 % 0.04 s 1 core @ 2.5 Ghz (Python)
185 gupnet_se 46.47 % 68.66 % 37.89 % 0.03s 1 core @ 2.5 Ghz (C/C++)
186 Disp R-CNN (velo)
This method uses stereo information.
 code 46.37 % 63.22 % 40.15 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
187 Disp R-CNN
This method uses stereo information.
 code 46.37 % 63.24 % 40.15 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
188 LT-M3OD 45.87 % 63.54 % 39.19 % 0.03 s 1 core @ 2.5 Ghz (Python)
189 SwinMono3D 45.72 % 67.95 % 38.55 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
190 CMKD 45.69 % 65.71 % 40.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
191 Lite-FPN-GUPNet 45.16 % 64.84 % 38.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
192 SparsePool code 44.57 % 60.53 % 40.37 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
193 MonoLCD 44.57 % 65.17 % 39.96 % 0.04 s 1 core @ 2.5 Ghz (Python)
194 Shift R-CNN (mono) code 42.96 % 63.24 % 38.22 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
195 D4LCN code 42.86 % 65.29 % 36.29 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
196 GCDR 42.78 % 67.11 % 37.94 % 0.28 s 1 core @ 2.5 Ghz (Python)
197 GUPNet code 42.78 % 67.11 % 37.94 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
198 MonoCon code 42.49 % 59.39 % 35.94 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
199 SAIC_ADC_Mono3D code 42.37 % 60.72 % 36.57 % 50 s GPU @ 2.5 Ghz (Python)
200 GAC3D++ 41.87 % 61.03 % 35.78 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
201 M3D-RPN code 41.54 % 61.54 % 35.23 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
202 MonoEF code 41.19 % 51.06 % 35.70 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
203 PS-fld 41.13 % 58.13 % 35.90 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
204 SOD 40.95 % 60.07 % 34.02 % 0.1 s 1 core @ 2.5 Ghz (Python)
205 MV-RGBD-RF
This method makes use of Velodyne laser scans.
 40.94 % 51.10 % 34.83 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
206 MK3D 40.32 % 62.70 % 35.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
207 PPTrans 39.55 % 48.93 % 33.74 % 0.2 s GPU @ 2.5 Ghz (Python)
208 RelationNet3D_dla34 code 39.52 % 59.56 % 34.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
209 ANM 39.39 % 57.77 % 34.59 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
210 OSE+ 39.26 % 58.13 % 34.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
211 CrazyTensor-ICN 38.69 % 59.42 % 33.90 % 1 s 1 core @ 2.5 Ghz (C/C++)
212 DDMP-3D 38.62 % 58.70 % 34.10 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
213 RelationNet3D_res18 code 37.41 % 56.22 % 32.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
214 mono3d code 37.18 % 49.10 % 32.04 % TBD TBD
215 AEC3D 37.15 % 48.48 % 34.86 % 18 ms GPU @ 2.5 Ghz (Python)
216 ICCV 36.70 % 53.31 % 31.94 % 0.04 s GPU @ 2.5 Ghz (Python)
217 Aug3D-RPN 36.69 % 51.49 % 30.04 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
218 SparsePool code 36.26 % 44.21 % 32.57 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
219 Y4 code 35.92 % 53.50 % 31.89 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
220 City-ICN 35.87 % 53.37 % 29.37 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
221 SS3D 35.48 % 52.97 % 31.07 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
222 DSGN
This method uses stereo information.
 code 35.15 % 49.10 % 31.41 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
223 pAUCEnsT 34.90 % 50.51 % 30.35 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
224 COF3D 32.97 % 51.77 % 28.26 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
225 TopNet-Retina
This method makes use of Velodyne laser scans.
 31.98 % 47.51 % 29.84 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
226 DFR-Net 31.93 % 48.34 % 27.95 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
227 VN3D 31.81 % 42.58 % 29.09 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
228 ZongmuMono3d code 31.56 % 44.68 % 27.48 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
229 BEVC 30.39 % 43.61 % 27.46 % 35ms GPU @ 1.5 Ghz (Python)
230 OC Stereo
This method uses stereo information.
 code 28.76 % 43.18 % 24.80 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
231 Vote3D
This method makes use of Velodyne laser scans.
 27.99 % 39.81 % 25.19 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
232 SGM3D 27.89 % 42.21 % 24.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
233 LSVM-MDPM-us code 27.81 % 37.66 % 24.83 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
234 DPM-VOC+VP 27.73 % 41.58 % 24.61 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
235 K3D 27.29 % 38.82 % 23.86 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
236 RefinedMPL 27.17 % 44.47 % 22.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
237 CaDDN code 27.13 % 40.03 % 23.23 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
238 PGD-FCOS3D code 26.48 % 44.28 % 23.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
239 LSVM-MDPM-sv 26.05 % 35.70 % 23.56 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
240 E2E-DA-Lite (Res18) 25.82 % 42.48 % 21.16 % 0.01 s GPU @ 2.5 Ghz (Python)
241 DPM-C8B1
This method uses stereo information.
 25.57 % 41.47 % 21.93 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
242 HBD 24.75 % 35.65 % 22.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
243 E2E-DA 24.46 % 39.34 % 19.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
244 MonoHMOO 23.59 % 37.41 % 21.20 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
245 Graph-NMS-baseline 23.07 % 35.72 % 20.54 % 47 ms GPU @ 2.5 Ghz (Python)
246 RT3D-GMP
This method uses stereo information.
 22.90 % 33.64 % 19.87 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
247 MAOLoss code 22.58 % 34.37 % 20.49 % 0.05 s 1 core @ 2.5 Ghz (Python)
248 Graph-NMS 22.43 % 34.13 % 19.65 % 36 ms GPU @ 2.5 Ghz (Python)
249 Lite-FPN 19.17 % 24.40 % 15.68 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
250 mBoW
This method makes use of Velodyne laser scans.
 17.63 % 26.66 % 16.02 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
251 CDTrack3D code 16.04 % 23.84 % 13.15 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
252 TopNet-HighRes
This method makes use of Velodyne laser scans.
 13.98 % 22.86 % 14.52 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
253 EGFN
This method uses stereo information.
 13.45 % 21.13 % 11.72 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
254 RT3DStereo
This method uses stereo information.
 12.96 % 19.58 % 11.47 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
255 TopNet-UncEst
This method makes use of Velodyne laser scans.
 12.00 % 18.14 % 11.85 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
256 MM 6.79 % 11.33 % 6.28 % 1 s 1 core @ 2.5 Ghz (C/C++)
257 YOLOv2 code 0.06 % 0.15 % 0.07 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
258 TopNet-DecayRate
This method makes use of Velodyne laser scans.
 0.04 % 0.00 % 0.04 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
Table as LaTeX | Only published Methods

Object Detection and Orientation Estimation Evaluation

Cars


Method Setting Code Moderate Easy Hard Runtime Environment
1 GraR-VoI 96.29 % 96.81 % 91.06 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
2 GraR-Po 96.09 % 96.83 % 90.99 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
3 SFD 96.05 % 98.95 % 90.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
4 VPFNet 96.04 % 96.63 % 90.99 % 0.06 s 2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. 2021.
5 Anonymous 96.01 % 99.06 % 90.97 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
6 Anonymous 96.00 % 98.72 % 90.97 % 0.1 s GPU @ 2.5 Ghz (Python)
7 CLOCs code 95.93 % 96.77 % 90.93 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
8 GraR-Vo 95.92 % 96.66 % 92.78 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
9 PE-RCVN 95.85 % 96.89 % 90.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
10 CityBrainLab 95.83 % 98.56 % 90.79 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
11 Anonymous 95.81 % 96.53 % 92.69 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
12 CLOCs_PVCas code 95.79 % 96.74 % 90.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
13 Anonymous 95.75 % 98.92 % 90.83 % n/a s 1 core @ 2.5 Ghz (C/C++)
14 GraR-Pi 95.72 % 98.57 % 92.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
15 SECOND 95.67 % 96.42 % 90.37 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
16 DVF-V 95.63 % 96.59 % 90.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 Anonymous 95.62 % 96.66 % 92.48 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
18 TBD 95.62 % 96.32 % 92.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
19 DSGN++
This method uses stereo information.
 95.58 % 98.04 % 88.09 % 0.4 s NVIDIA Tesla V100
20 Fast-CLOCs 95.57 % 96.66 % 90.70 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
21 CasA 95.53 % 96.51 % 92.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 EA-M-RCNN(BorderAtt) 95.44 % 96.37 % 90.31 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
23 JPVNet 95.38 % 96.40 % 90.52 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
24 DVF-PV 95.35 % 96.40 % 92.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 TBD 95.34 % 96.05 % 90.25 % 0.1 s 1 core @ 2.5 Ghz (Python)
26 ISE-RCNN-PV 95.34 % 96.18 % 92.70 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
27 BADet code 95.34 % 98.65 % 90.28 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition (PR) 2022.
28 Anonymous 95.32 % 98.34 % 90.36 % 0.1s 1 core @ 2.5 Ghz (C/C++)
29 LGNet 95.31 % 96.51 % 92.55 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
30 ISE-RCNN 95.30 % 96.36 % 92.63 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
31 Anonymous code 95.23 % 96.29 % 92.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
32 TBD 95.20 % 96.16 % 90.42 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
33 EQ-PVRCNN 95.20 % 98.22 % 92.47 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
34 anonymous 95.19 % 96.35 % 92.38 % 0.09 s GPU @ 2.5 Ghz (Python)
35 SE-SSD
This method makes use of Velodyne laser scans.
 code 95.17 % 96.55 % 90.00 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
36 DGDNH 95.15 % 98.35 % 92.55 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
37 PTA-RCNN 95.13 % 96.30 % 92.44 % 0.08 s 1 core @ 2.5 Ghz (Python)
38 VoxSeT 95.13 % 96.15 % 90.38 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
39 SPANet 95.03 % 96.31 % 89.99 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
40 Pyramid R-CNN 95.03 % 95.87 % 92.46 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
41 VPFNet code 95.01 % 96.03 % 92.41 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
42 EPNet++ 95.00 % 96.70 % 91.82 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
43 Voxel R-CNN code 94.96 % 96.47 % 92.24 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
44 PDV 94.91 % 96.06 % 92.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 PV-RCNN++ 94.90 % 96.07 % 92.22 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
46 SIENet code 94.85 % 96.01 % 92.23 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
47 VoTr-TSD code 94.81 % 95.95 % 92.24 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
48 USVLab BSAODet (MM) 94.79 % 96.18 % 92.05 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
49 GVNet-V2 94.77 % 96.28 % 91.98 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
50 CSVoxel-RCNN 94.74 % 96.31 % 91.84 % 0.03 s GPU @ 1.0 Ghz (Python)
51 TBD 94.73 % 97.01 % 92.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 GVNet code 94.72 % 96.29 % 92.00 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
53 SRIF-RCNN 94.70 % 95.62 % 92.21 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
54 M3DeTR code 94.70 % 97.37 % 91.89 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
55 ST-RCNN
This method makes use of Velodyne laser scans.
 94.69 % 98.05 % 91.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
56 ST-RCNN (SNLW-RCNN)
This method makes use of Velodyne laser scans.
 code 94.69 % 98.05 % 91.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
57 DCAnet 94.67 % 95.74 % 92.10 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
58 TPCG 94.66 % 95.95 % 92.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 XView 94.66 % 95.88 % 92.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
60 MSG-PGNN 94.65 % 95.85 % 92.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
61 StructuralIF 94.64 % 96.12 % 91.85 % 0.02 s 8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
62 VCRCNN 94.64 % 96.05 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 SqueezeRCNN 94.61 % 96.02 % 91.97 % 0.08 s 1 core @ 2.5 Ghz (Python)
64 FusionDetv2-v4 94.60 % 95.92 % 91.79 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
65 TBD 94.60 % 95.87 % 91.71 % 0.06 s GPU @ 2.5 Ghz (Python)
66 Generalized-SIENet 94.59 % 95.74 % 91.99 % 0.08 s 1 core @ 2.5 Ghz (Python)
67 HyBrid Feature Det 94.59 % 95.87 % 91.97 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
68 P2V-RCNN 94.59 % 96.01 % 92.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
69 CAT-Det 94.57 % 95.95 % 91.88 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
70 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 94.57 % 98.15 % 91.85 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
71 LZY_RCNN 94.56 % 95.80 % 91.94 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
72 SCIR-Net
This method makes use of Velodyne laser scans.
 94.55 % 96.11 % 91.68 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
73 DDet 94.54 % 95.80 % 91.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 TransCyclistNet 94.52 % 96.07 % 91.94 % 0.08 s 1 core @ 2.5 Ghz (Python)
75 Fast VP-RCNN code 94.52 % 97.99 % 91.74 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
76 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 94.52 % 95.84 % 91.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
77 FusionDetv2-v3 94.51 % 96.14 % 91.73 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
78 TransDet3D 94.50 % 95.82 % 91.89 % 0.08 s 1 core @ 2.5 Ghz (Python)
79 Point Image Fusion 94.49 % 95.69 % 91.92 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
80 FPV-SSD 94.48 % 96.89 % 91.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
81 MVRA + I-FRCNN+ 94.46 % 95.66 % 81.74 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
82 SVGA-Net 94.45 % 96.02 % 91.54 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
83 SA-voxel-centernet code 94.45 % 95.78 % 91.84 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
84 WHUT-iou_ssd code 94.45 % 95.76 % 91.75 % 0.045s 1 core @ 2.5 Ghz (C/C++)
85 DVFENet 94.44 % 95.33 % 91.55 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
86 anonymous code 94.43 % 97.50 % 91.66 % 0.05s 1 core @ >3.5 Ghz (python)
87 RangeIoUDet
This method makes use of Velodyne laser scans.
 94.42 % 95.69 % 91.70 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
88 FPC-RCNN 94.40 % 96.13 % 91.63 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
89 sa-voxel-centernet code 94.39 % 95.86 % 91.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
90 FPC3D
This method makes use of the epipolar geometry.
 94.39 % 96.04 % 91.51 % 33 s 1 core @ 2.5 Ghz (C/C++)
91 USVLab BSAODet (SM) 94.36 % 96.06 % 89.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
92 SPVB-SSD 94.31 % 95.78 % 91.61 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
93 SAA-SECOND 94.24 % 95.64 % 91.40 % 38m s 1 core @ 2.5 Ghz (C/C++)
94 SERCNN
This method makes use of Velodyne laser scans.
 94.24 % 96.31 % 89.71 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
95 EPNet code 94.22 % 96.13 % 89.68 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
96 TCDVF 94.18 % 95.00 % 91.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
97 TBD 94.09 % 94.97 % 91.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
98 SRDL 94.08 % 95.83 % 91.55 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
99 FusionDetv1 94.07 % 95.82 % 91.54 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
100 SARFE 94.07 % 95.73 % 91.40 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
101 FusionDetv2-v2 94.05 % 95.73 % 89.66 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
102 SAA-PV-RCNN 94.02 % 95.00 % 92.34 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
103 FPCR-CNN 93.99 % 95.94 % 90.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
104 NV2P-RCNN 93.91 % 97.80 % 90.96 % 0.1 s GPU @ 2.5 Ghz (Python)
105 RangeRCNN
This method makes use of Velodyne laser scans.
 93.90 % 95.47 % 91.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
106 DGT-Det3D 93.89 % 95.53 % 91.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
107 TBD 93.80 % 94.52 % 86.34 % 0.1 s 1 core @ 2.5 Ghz (Python)
108 SIF 93.79 % 95.48 % 91.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
109 DD3D code 93.78 % 94.67 % 88.99 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
110 MGAF-3DSSD code 93.77 % 94.45 % 86.25 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
111 MMLAB LIGA-Stereo
This method uses stereo information.
 code 93.71 % 96.40 % 86.00 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
112 CAD 93.63 % 96.82 % 88.59 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
113 DKAnet 93.59 % 95.10 % 88.98 % 0.05 s 1 core @ 2.0 Ghz (Python)
114 Patches - EMP
This method makes use of Velodyne laser scans.
 93.58 % 97.88 % 90.31 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
115 SA3DNet
This method uses stereo information.
This method makes use of Velodyne laser scans.
 93.55 % 96.56 % 88.56 % 0.05 s GPU @ 2.5 Ghz (Python)
116 LIGA-Stereo-old
This method uses stereo information.
 93.54 % 96.63 % 83.68 % 0.375 s Titan Xp
117 MVAF-Net code 93.54 % 95.35 % 90.70 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
118 TF3D
This method makes use of Velodyne laser scans.
 93.49 % 96.57 % 88.54 % 0.1 s 2 cores @ 3.0 Ghz (Python)
119 IA-SSD (multi) 93.47 % 96.07 % 90.51 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
120 Associate-3Ddet_v2 93.46 % 96.66 % 88.20 % 0.04 s 1 core @ 2.5 Ghz (Python)
121 FusionDetv2-v5 93.44 % 95.31 % 88.96 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
122 IA-SSD (single) 93.41 % 96.23 % 88.34 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
123 DVF 93.39 % 96.44 % 88.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
124 TBD
This method makes use of Velodyne laser scans.
 93.34 % 96.51 % 90.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
125 CIA-SSD
This method makes use of Velodyne laser scans.
 code 93.34 % 96.65 % 85.76 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
126 TBD 93.31 % 94.17 % 88.30 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
127 Deep MANTA 93.31 % 98.83 % 82.95 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
128 Sem-Aug v1 code 93.26 % 96.36 % 90.51 % 0.04 s GPU @ 3.5 Ghz (Python)
129 LPCG-Monoflex 93.26 % 96.68 % 83.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
130 DSASNet 93.25 % 96.54 % 88.34 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
131 FS-Net
This method makes use of Velodyne laser scans.
 93.21 % 96.38 % 90.49 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
132 CityBrainLab-CT3D code 93.20 % 96.26 % 90.44 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
133 TBD 93.20 % 95.96 % 90.30 % TBD GPU @ 2.5 Ghz (Python + C/C++)
134 AM-SSD 93.18 % 96.56 % 90.13 % 0.04 s 1 core @ 2.5 Ghz (Python)
135 demo 93.15 % 96.15 % 90.08 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
136 VCT 93.09 % 96.30 % 90.38 % 0.2 s 1 core @ 2.5 Ghz (Python)
137 VPN 93.08 % 96.16 % 88.01 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
138 MVOD 93.07 % 96.15 % 92.39 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
139 KpNet 93.06 % 96.63 % 85.39 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
140 KpNet 93.05 % 96.63 % 85.38 % 42 s 1 core @ 2.5 Ghz (C/C++)
141 H^23D R-CNN code 93.03 % 96.13 % 90.33 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
142 ASCNet 93.01 % 96.05 % 90.21 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
143 FromVoxelToPoint code 92.98 % 96.07 % 90.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
144 SGNet 92.95 % 96.41 % 90.35 % 0.09 s GPU @ 2.5 Ghz (Python)
145 Sem-Aug-PointRCNN code 92.92 % 95.66 % 88.07 % 0.1 s GPU @ 3.5 Ghz (C/C++)
146 EBM3DOD code 92.88 % 96.39 % 87.58 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
147 Struc info fusion II 92.88 % 96.44 % 87.67 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
148 MBDF-Net 92.77 % 96.19 % 89.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
149 YF 92.74 % 96.02 % 89.76 % 0.04 s GPU @ 2.5 Ghz (C/C++)
150 HotSpotNet 92.74 % 96.20 % 89.68 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
151 Struc info fusion I 92.71 % 96.24 % 87.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
152 EBM3DOD baseline code 92.70 % 96.31 % 87.44 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
153 MDNet 92.62 % 96.09 % 85.09 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
154 SARPNET 92.58 % 95.82 % 87.33 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
155 Patches
This method makes use of Velodyne laser scans.
 92.57 % 96.31 % 87.41 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
156 R-GCN 92.53 % 96.16 % 87.45 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
157 PI-RCNN 92.52 % 96.15 % 87.47 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
158 CenterNet3D 92.48 % 95.71 % 89.54 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
159 MSADet 92.45 % 96.08 % 89.27 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
160 PointPainting
This method makes use of Velodyne laser scans.
 92.43 % 98.36 % 89.49 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
161 3D IoU-Net 92.42 % 96.31 % 87.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
162 MBDF-Net-1 92.37 % 95.87 % 89.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
163 CLOCs_SecCas 92.37 % 95.16 % 88.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
164 NV-RCNN 92.34 % 95.83 % 89.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
165 DASS 92.25 % 96.20 % 87.26 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
166 S-AT GCN 92.24 % 95.02 % 90.46 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
167 3D-VDNet 92.19 % 95.39 % 89.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
168 SegVoxelNet 92.16 % 95.86 % 86.90 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
169 LazyTorch-CP-Small-P 92.15 % 94.64 % 89.71 % 1 s 1 core @ 2.5 Ghz (C/C++)
170 PointRGCN 92.15 % 97.48 % 86.83 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
171 LazyTorch-CP-Infer-O 92.12 % 94.69 % 89.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
172 CSNet 92.06 % 95.87 % 88.62 % 0.1 s 1 core @ 2.5 Ghz (Python)
173 F-ConvNet
This method makes use of Velodyne laser scans.
 code 91.98 % 95.81 % 79.83 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
174 RangeDet code 91.92 % 95.16 % 86.98 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
175 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 91.87 % 95.86 % 86.78 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
176 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 91.77 % 95.90 % 86.92 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
177 KAIST-VDCLab 91.74 % 95.40 % 84.25 % 0.04 s 1 core @ 2.5 Ghz (Python)
178 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 91.73 % 95.00 % 88.86 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
179 CrazyTensor-CP 91.73 % 93.62 % 88.94 % 1 s 1 core @ 2.5 Ghz (Python)
180 Dune-DCF-e09 91.70 % 94.92 % 88.44 % 1 s 1 core @ 2.5 Ghz (C/C++)
181 TBD 91.67 % 94.52 % 86.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
182 CrazyTensor-CF 91.66 % 94.86 % 88.37 % 1 s 1 core @ 2.5 Ghz (C/C++)
183 AutoAlign 91.60 % 95.07 % 88.86 % 0.1 s 1 core @ 2.5 Ghz (Python)
184 C-GCN 91.57 % 95.63 % 86.13 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
185 CVFNet 91.55 % 95.20 % 87.80 % 28.1ms 1 core @ 2.5 Ghz (Python)
186 Dune-DCF-e11 91.49 % 94.64 % 88.28 % 1 s 1 core @ 2.5 Ghz (C/C++)
187 Dune-DCF-e15 91.48 % 94.68 % 88.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
188 TBD 91.48 % 94.51 % 86.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
189 City-CF-fixed 91.48 % 94.81 % 88.51 % 1 s 1 core @ 2.5 Ghz (C/C++)
190 LazyTorch-CP 91.36 % 93.93 % 88.83 % 1 s 1 core @ 2.5 Ghz (C/C++)
191 PointRGBNet 91.33 % 95.39 % 86.29 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
192 EgoNet code 91.23 % 96.11 % 80.96 % 0.1 s GPU @ 1.5 Ghz (Python)
S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
193 City-CF 91.18 % 94.49 % 88.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
194 AIMC-RUC 91.18 % 96.84 % 85.94 % 0.11 s 1 core @ 2.5 Ghz (Python)
195 CA3D 91.08 % 95.05 % 81.53 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
196 Contrastive PP code 91.06 % 96.86 % 87.69 % 0.01 s 1 core @ 2.5 Ghz (Python)
197 PFF3D
This method makes use of Velodyne laser scans.
 code 91.06 % 94.86 % 86.28 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
198 PP-PCdet code 91.05 % 94.77 % 87.72 % 0.01 s 1 core @ 2.5 Ghz (Python)
199 Stereo CenterNet
This method uses stereo information.
 91.02 % 96.54 % 83.15 % 0.04 s GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
200 sscl-20p 90.94 % 96.89 % 87.60 % 0.02 s 1 core @ 2.5 Ghz (Python)
201 Mix-Teaching-M3D 90.84 % 96.31 % 83.11 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
202 MonoFlex 90.82 % 95.95 % 83.11 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
203 SCSTSV-MonoFlex 90.81 % 96.36 % 80.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
204 MAFF-Net(DAF-Pillar) 90.78 % 94.17 % 83.17 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
205 HRI-VoxelFPN 90.76 % 96.35 % 85.37 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
206 KM3D code 90.70 % 96.34 % 80.72 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
207 PointPillars
This method makes use of Velodyne laser scans.
 code 90.70 % 93.84 % 87.47 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
208 WS3D
This method makes use of Velodyne laser scans.
 90.69 % 94.85 % 85.94 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
209 MonoEF code 90.65 % 96.19 % 82.95 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
210 FPC3D_all
This method makes use of Velodyne laser scans.
 90.60 % 95.35 % 85.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
211 QD-3DT
This is an online method (no batch processing).
 code 90.49 % 92.61 % 80.32 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
212 vadin-TBD 90.49 % 95.86 % 80.66 % 0.04 s 1 core @ 2.5 Ghz (Python)
213 MonoGround 90.42 % 93.87 % 80.54 % 0.03 s 1 core @ 2.5 Ghz (Python)
214 MonoDistill 90.33 % 95.82 % 80.38 % 0.04 s 1 core @ 2.5 Ghz (Python)
215 MonoEdge 90.32 % 93.43 % 80.57 % 0.05 s GPU @ 2.5 Ghz (Python)
216 MonoFlex 90.29 % 95.51 % 82.68 % 0.03 s 1 core @ 2.5 Ghz (Python)
217 3D_att
This method makes use of Velodyne laser scans.
 90.26 % 96.42 % 84.81 % 0.17 s GPU @ 2.5 Ghz (Python)
218 monodle code 90.23 % 93.46 % 80.11 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
219 3D IoU Loss
This method makes use of Velodyne laser scans.
 90.21 % 95.60 % 84.96 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
220 MonoCInIS 90.20 % 95.80 % 82.00 % 0,13 s GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
221 ARPNET 90.11 % 93.42 % 82.56 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
222 TANet code 90.11 % 93.52 % 84.61 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
223 MonoEdge-Rotate 90.07 % 93.46 % 80.29 % 0.05 s GPU @ 2.5 Ghz (Python)
224 mono3d 90.02 % 93.54 % 83.19 % 0.03 s GPU @ 2.5 Ghz (Python)
225 CG-Stereo
This method uses stereo information.
 89.98 % 96.28 % 82.21 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
226 Deep3DBox 89.88 % 94.62 % 76.40 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
227 PS-fld 89.78 % 95.60 % 81.68 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
228 GPP code 89.68 % 93.94 % 80.60 % 0.23 s GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.
229 SubCNN 89.53 % 94.11 % 79.14 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
230 MonoGeo 89.44 % 94.67 % 79.27 % 0.05 s 1 core @ 2.5 Ghz (Python)
231 SCNet
This method makes use of Velodyne laser scans.
 89.36 % 95.23 % 84.03 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
232 Geo3D 89.28 % 93.60 % 77.21 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
233 GA-Aug 89.24 % 92.66 % 81.31 % 0.04 s GPU @ 2.5 Ghz (Python)
234 TBD_BD code 89.23 % 94.01 % 86.17 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
235 Digging_M3D 89.23 % 93.54 % 79.15 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
236 AVOD
This method makes use of Velodyne laser scans.
 code 89.22 % 94.98 % 82.14 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
237 IAFA 89.14 % 92.96 % 79.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
238 MonoDDE 89.07 % 96.72 % 81.42 % 0.04 s 1 core @ 2.5 Ghz (Python)
239 FusionDetv2-v1 89.00 % 94.78 % 84.10 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
240 PPTrans 88.70 % 95.04 % 81.15 % 0.2 s GPU @ 2.5 Ghz (Python)
241 GAC3D++ 88.69 % 94.16 % 78.74 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
242 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 88.61 % 94.65 % 83.71 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
243 FusionDetv2-baseline 88.58 % 94.20 % 85.36 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
244 M3DSSD++ code 88.04 % 94.75 % 76.03 % 0.16s 1 core @ 2.5 Ghz (C/C++)
245 MonoCon code 87.92 % 93.52 % 75.83 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
246 Lite-FPN-GUPNet 87.90 % 96.08 % 75.63 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
247 MonoLCD 87.86 % 93.62 % 78.09 % 0.04 s 1 core @ 2.5 Ghz (Python)
248 CMKD 87.83 % 94.87 % 80.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
249 DeepStereoOP 87.81 % 93.68 % 77.60 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
250 CMKD 87.79 % 95.17 % 80.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
251 MonoRUn code 87.64 % 95.44 % 77.75 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
252 3DBN
This method makes use of Velodyne laser scans.
 87.59 % 93.34 % 79.91 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
253 FQNet 87.49 % 93.66 % 73.61 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
254 Shift R-CNN (mono) code 87.47 % 93.75 % 77.19 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
255 MonoPSR code 87.45 % 93.29 % 72.26 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
256 Mono3D code 87.28 % 93.13 % 77.00 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
257 SAIC_ADC_Mono3D code 87.27 % 95.04 % 79.50 % 50 s GPU @ 2.5 Ghz (Python)
258 SMOKE code 87.02 % 92.94 % 77.12 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
259 3DOP
This method uses stereo information.
 code 86.93 % 91.31 % 76.72 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
260 CDN
This method uses stereo information.
 code 86.90 % 95.79 % 79.05 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
261 RTM3D code 86.73 % 91.75 % 77.18 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
262 MonoDTR 86.70 % 93.12 % 74.53 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
263 mono3d code 86.56 % 93.12 % 77.11 % TBD TBD
264 MonoRCNN code 86.48 % 91.90 % 66.71 % 0.07 s GPU @ 2.5 Ghz (Python)