3D Object Detection Evaluation 2017


The 3D object detection benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. 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 3D object detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require an 3D bounding box overlap of 70%, while for pedestrians and cyclists we require a 3D bounding box overlap of 50%. 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 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 TED 85.28 % 91.61 % 80.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 LIVOX_Det
This method makes use of Velodyne laser scans.
84.94 % 91.72 % 80.10 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
3 SFD code 84.76 % 91.73 % 77.92 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
4 Anonymous 84.76 % 91.99 % 79.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
5 CasA++ 84.04 % 90.68 % 79.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 Anonymous 83.96 % 90.83 % 77.47 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
7 DGDNH 83.88 % 90.69 % 79.50 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
8 Anonymous 83.51 % 89.08 % 78.94 % n/a s 1 core @ 2.5 Ghz (C/C++)
9 GraR-VoI 83.27 % 91.89 % 77.78 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
10 GLENet-VR 83.23 % 91.67 % 78.43 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
11 VPFNet 83.21 % 91.02 % 78.20 % 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.
12 GraR-Po 83.18 % 91.79 % 77.98 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
13 CasA 83.06 % 91.58 % 80.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
14 BtcDet
This method makes use of Velodyne laser scans.
code 82.86 % 90.64 % 78.09 % 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.
15 GraR-Vo 82.77 % 91.29 % 77.20 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
16 3SNet 82.70 % 89.41 % 78.03 % 0.07 s GPU @ 2.5 Ghz (Python)
17 PE-RCVN 82.69 % 91.51 % 77.75 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
18 CAD 82.68 % 88.96 % 77.91 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
19 SPG_mini
This method makes use of Velodyne laser scans.
code 82.66 % 90.64 % 77.91 % 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.
20 SGFusion 82.64 % 91.13 % 77.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
21 HCPVF 82.63 % 89.34 % 77.72 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
22 DSASNet 82.63 % 89.48 % 77.94 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
23 SA3DNet
This method uses stereo information.
This method makes use of Velodyne laser scans.
82.57 % 90.49 % 77.88 % 0.05 s GPU @ 2.5 Ghz (Python)
24 SE-SSD
This method makes use of Velodyne laser scans.
code 82.54 % 91.49 % 77.15 % 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 TF3D
This method makes use of Velodyne laser scans.
82.46 % 89.10 % 77.78 % 0.1 s 2 cores @ 3.0 Ghz (Python)
26 DVF-V 82.45 % 89.40 % 77.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. 2022.
27 GraR-Pi 82.42 % 90.94 % 77.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
28 DVF-PV 82.40 % 90.99 % 77.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. 2022.
29 Anonymous 82.30 % 90.88 % 76.89 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
30 PVT-SSD 82.29 % 90.65 % 76.85 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
31 Focals Conv code 82.28 % 90.55 % 77.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
32 CLOCs code 82.28 % 89.16 % 77.23 % 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.
33 CityBrainLab 82.22 % 90.54 % 77.19 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
34 SPT 82.18 % 90.52 % 77.62 % 0.1 s GPU @ 2.5 Ghz (Python)
35 SASA
This method makes use of Velodyne laser scans.
code 82.16 % 88.76 % 77.16 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
36 ImpDet 82.14 % 88.39 % 76.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 SPG
This method makes use of Velodyne laser scans.
code 82.13 % 90.50 % 78.90 % 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.
38 SPNet code 82.11 % 88.53 % 77.41 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
39 VoTr-TSD code 82.09 % 89.90 % 79.14 % 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.
40 TBD 82.09 % 89.50 % 79.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
41 Pyramid R-CNN 82.08 % 88.39 % 77.49 % 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.
42 FS-Net
This method makes use of Velodyne laser scans.
82.07 % 88.68 % 77.42 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
43 VoxSeT code 82.06 % 88.53 % 77.46 % 33 ms 1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
44 SRIF-RCNN 82.04 % 88.45 % 77.54 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
X. Li and D. Kong: SRIF-RCNN: Sparsely Represented Inputs Fusion of Different Sensors for 3D Object Detection. Applied Intelligence 2022.
45 LGNet 82.02 % 90.65 % 77.34 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
46 EQ-PVRCNN code 82.01 % 90.13 % 77.53 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
47 anonymous 81.99 % 88.82 % 77.26 % 0.09 s GPU @ 2.5 Ghz (Python)
48 EPNet++ 81.96 % 91.37 % 76.71 % 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.
49 HMFI code 81.93 % 88.90 % 77.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 PV-RCNN++ code 81.88 % 90.14 % 77.15 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
51 GLENet 81.86 % 89.87 % 77.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
52 PDV code 81.86 % 90.43 % 77.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
53 SGNet 81.85 % 88.83 % 77.47 % 0.09 s GPU @ 2.5 Ghz (Python)
54 Anonymous 81.85 % 89.96 % 76.51 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
55 ST-RCNN
This method makes use of Velodyne laser scans.
81.84 % 90.50 % 77.22 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
56 ISE-RCNN 81.83 % 89.12 % 77.29 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
57 FV2P v2 81.81 % 88.17 % 77.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
58 Anonymous 81.80 % 89.86 % 77.26 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
59 CityBrainLab-CT3D code 81.77 % 87.83 % 77.16 % 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.
60 GV-RCNN code 81.75 % 90.31 % 77.17 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
61 USVLab BSAODet 81.74 % 88.89 % 77.14 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
62 JPVNet 81.73 % 88.66 % 76.94 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
63 TBD 81.73 % 89.48 % 79.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 M3DeTR code 81.73 % 90.28 % 76.96 % 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.
65 SIENet code 81.71 % 88.22 % 77.22 % 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.
66 TBD 81.71 % 88.46 % 76.63 % 0.1 s 1 core @ 2.5 Ghz (Python)
67 DCCA 81.70 % 88.42 % 77.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
68 VCRCNN 81.68 % 90.52 % 77.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
69 Voxel R-CNN code 81.62 % 90.90 % 77.06 % 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.
70 BADet code 81.61 % 89.28 % 76.58 % 0.14 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 2022.
71 SARFE 81.59 % 88.88 % 76.74 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
72 FromVoxelToPoint code 81.58 % 88.53 % 77.37 % 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.
73 H^23D R-CNN code 81.55 % 90.43 % 77.22 % 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.
74 Anonymous 81.55 % 87.90 % 77.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 81.46 % 88.25 % 76.96 % 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.
76 P2V-RCNN 81.45 % 88.34 % 77.20 % 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.
77 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 81.43 % 90.25 % 76.82 % 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.
78 DDet 81.38 % 89.63 % 78.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 XView 81.35 % 89.21 % 76.87 % 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.
80 ISE-RCNN-PV 81.34 % 88.05 % 76.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
81 RangeRCNN
This method makes use of Velodyne laser scans.
81.33 % 88.47 % 77.09 % 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.
82 CAT-Det 81.32 % 89.87 % 76.68 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
83 GT3D 81.24 % 87.61 % 74.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
84 IKT3D
This method makes use of Velodyne laser scans.
80.97 % 87.84 % 76.43 % 0.05 s 1 core @ 2.5 Ghz (Python)
85 VPFNet code 80.97 % 88.51 % 76.74 % 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.
86 GVNet-V2 80.96 % 87.57 % 76.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
87 VueronNet code 80.96 % 90.06 % 73.72 % 0.06 s 1 core @ 2.0 Ghz (Python)
88 DKDet 80.94 % 87.66 % 76.23 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
89 FusionDetv2-v4 80.93 % 87.75 % 76.12 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
90 NV-RCNN 80.78 % 86.35 % 76.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
91 Sem-Aug 80.77 % 89.41 % 75.90 % 0.08 s GPU @ 2.5 Ghz (Python)
92 CSVoxel-RCNN 80.73 % 87.44 % 76.18 % 0.03 s GPU @ 1.0 Ghz (Python)
93 StructuralIF 80.69 % 87.15 % 76.26 % 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.
94 SPVB-SSD 80.68 % 86.99 % 76.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
95 CLOCs_PVCas code 80.67 % 88.94 % 77.15 % 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.
96 GVNet code 80.52 % 87.63 % 75.99 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
97 SVGA-Net 80.47 % 87.33 % 75.91 % 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.
98 SC-Voxel-RCNN 80.46 % 86.94 % 75.85 % 0.12 s GPU @ 1.0 Ghz (Python)
99 TBD 80.44 % 88.83 % 73.18 % 0.1 s 1 core @ 2.5 Ghz (Python)
100 Sem-Aug v1 code 80.40 % 88.92 % 77.37 % 0.04 s GPU @ 3.5 Ghz (Python)
101 SRDL 80.38 % 87.73 % 76.27 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
102 Fast-CLOCs 80.35 % 89.10 % 76.99 % 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.
103 FPV-SSD 80.34 % 87.72 % 75.40 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
104 SPANet 80.34 % 91.05 % 74.89 % 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.
105 IA-SSD (single) code 80.32 % 88.87 % 75.10 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
106 TBD 80.29 % 87.37 % 73.05 % 0.1 s 1 core @ 2.5 Ghz (Python)
107 CIA-SSD
This method makes use of Velodyne laser scans.
code 80.28 % 89.59 % 72.87 % 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.
108 FusionDetv1 80.28 % 87.45 % 76.21 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
109 DVF 80.21 % 88.97 % 75.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
110 PVTr 80.16 % 86.90 % 75.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
111 IA-SSD (multi) code 80.13 % 88.34 % 75.04 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
112 EBM3DOD code 80.12 % 91.05 % 72.78 % 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.
113 TBD 80.12 % 86.50 % 75.72 % 0.06 s GPU @ 2.5 Ghz (Python)
114 ATT_SSD 80.11 % 88.94 % 74.91 % 0.01 s 1 core @ 2.5 Ghz (Python)
115 TBD code 80.06 % 88.75 % 74.84 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
116 3D-CVF at SPA
This method makes use of Velodyne laser scans.
80.05 % 89.20 % 73.11 % 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.
117 SIF 79.88 % 86.84 % 75.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
118 RangeIoUDet
This method makes use of Velodyne laser scans.
79.80 % 88.60 % 76.76 % 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.
119 SA-SSD code 79.79 % 88.75 % 74.16 % 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.
120 DGT-Det3D 79.78 % 86.76 % 75.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
121 KpNet 79.75 % 88.92 % 72.17 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
122 KpNet 79.74 % 88.88 % 72.13 % 42 s 1 core @ 2.5 Ghz (C/C++)
123 STD code 79.71 % 87.95 % 75.09 % 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.
124 MGAF-3DSSD code 79.68 % 88.16 % 72.39 % 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.
125 DGCN 79.67 % 88.33 % 74.30 % 0.1 s GPU @ 2.5 Ghz (Python)
126 mbdf-netv1 code 79.66 % 90.19 % 74.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
127 PTA-RCNN 79.61 % 87.84 % 74.43 % 0.08 s 1 core @ 2.5 Ghz (Python)
128 Struc info fusion II 79.59 % 88.97 % 72.51 % 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.
129 3DSSD code 79.57 % 88.36 % 74.55 % 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.
130 EBM3DOD baseline code 79.52 % 88.80 % 72.30 % 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.
131 Struc info fusion I 79.49 % 88.70 % 74.25 % 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.
132 Point-GNN
This method makes use of Velodyne laser scans.
code 79.47 % 88.33 % 72.29 % 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.
133 SECOND 79.46 % 87.44 % 73.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
134 DCAN-Second code 79.40 % 88.57 % 75.05 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
135 AGS-SSD[la] 79.39 % 88.13 % 74.11 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
136 SSL-PointGNN code 79.36 % 87.78 % 74.15 % 0.56 s GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
137 USVLab BSAODet (S) 79.30 % 88.02 % 76.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
138 EPNet code 79.28 % 89.81 % 74.59 % 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.
139 WGVRF 79.25 % 88.47 % 74.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
140 DVFENet 79.18 % 86.20 % 74.58 % 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.
141 ITCA-SSD code 79.11 % 88.66 % 72.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
142 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 79.05 % 87.45 % 76.14 % 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. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
143 3D IoU-Net 79.03 % 87.96 % 72.78 % 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.
144 SERCNN
This method makes use of Velodyne laser scans.
78.96 % 87.74 % 74.30 % 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.
145 NV2P-RCNN 78.92 % 87.36 % 74.16 % 0.1 s GPU @ 2.5 Ghz (Python)
146 MVMM code 78.87 % 87.59 % 73.78 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
147 MSADet 78.81 % 88.31 % 73.82 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
148 PSA-Det3D 78.80 % 87.46 % 74.47 % 0.1 s GPU @ 2.5 Ghz (Python)
149 CSNet8306 code 78.74 % 89.57 % 72.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
150 MVAF-Net code 78.71 % 87.87 % 75.48 % 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.
151 CenterFuse 78.70 % 86.92 % 73.87 % 0.059 sec/frame 2 x V100
152 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.49 % 87.81 % 73.51 % 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.
153 CLOCs_SecCas 78.45 % 86.38 % 72.45 % 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.
154 CSNet 78.42 % 87.39 % 71.75 % 0.1 s 1 core @ 2.5 Ghz (Python)
155 Patches - EMP
This method makes use of Velodyne laser scans.
78.41 % 89.84 % 73.15 % 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.
156 CZY 78.36 % 87.00 % 73.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 HotSpotNet 78.31 % 87.60 % 73.34 % 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.
158 FusionDetv2-v5 78.30 % 86.94 % 73.44 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
159 Sem-Aug-PointRCNN++ 78.06 % 86.69 % 73.85 % 0.1 s 8 cores @ 3.0 Ghz (Python)
160 CF-cd-io-tv 78.05 % 86.38 % 73.29 % 1 s 1 core @ 2.5 Ghz (C/C++)
161 VPN 77.93 % 85.02 % 72.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
162 CenterNet3D 77.90 % 86.20 % 73.03 % 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.
163 TBD 77.85 % 86.46 % 72.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
164 CF-ctdep-tv-ta 77.75 % 85.27 % 74.83 % 1 s 1 core @ 2.5 Ghz (C/C++)
165 IoU-2B 77.74 % 85.65 % 71.30 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
166 Reprod-Two-Branch 77.73 % 85.60 % 74.24 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
167 AutoAlign 77.58 % 86.84 % 73.23 % 0.1 s 1 core @ 2.5 Ghz (Python)
168 TBD 77.56 % 85.38 % 72.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
169 CFF-tv 77.53 % 85.01 % 74.01 % 1 s 1 core @ 2.5 Ghz (C/C++)
170 TCDVF 77.49 % 85.55 % 72.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
171 CFF-ep25 77.48 % 84.84 % 72.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
172 UberATG-MMF
This method makes use of Velodyne laser scans.
77.43 % 88.40 % 70.22 % 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.
173 CFF-tv-v2 77.41 % 85.18 % 72.81 % 1 s 1 core @ 2.5 Ghz (C/C++)
174 Associate-3Ddet code 77.40 % 85.99 % 70.53 % 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.
175 Fast Point R-CNN
This method makes use of Velodyne laser scans.
77.40 % 85.29 % 70.24 % 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.
176 cff-tv-v2-ep25 77.38 % 84.44 % 72.82 % 1 s 1 core @ 2.5 Ghz (C/C++)
177 3D_att
This method makes use of Velodyne laser scans.
77.27 % 88.46 % 70.11 % 0.17 s GPU @ 2.5 Ghz (Python)
178 cp-tv-kp-io-sc 77.25 % 85.41 % 72.42 % 1 s 1 core @ 2.5 Ghz (C/C++)
179 Patches
This method makes use of Velodyne laser scans.
77.20 % 88.67 % 71.82 % 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.
180 CF-ctdep-tv 77.12 % 84.71 % 74.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
181 CF-base-tv 77.09 % 83.72 % 73.71 % 1 s 1 core @ 2.5 Ghz (C/C++)
182 Sem-Aug-PointRCNN code 77.04 % 82.75 % 73.21 % 0.1 s GPU @ 3.5 Ghz (C/C++)
183 KeyFuse2B 76.95 % 84.86 % 72.53 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
184 KeyPoint-IoUHead 76.81 % 84.61 % 72.16 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
185 HRI-VoxelFPN 76.70 % 85.64 % 69.44 % 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.
186 DKAnet 76.70 % 84.57 % 71.54 % 0.05 s 1 core @ 2.0 Ghz (Python)
187 cff-tv-t 76.68 % 85.58 % 70.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
188 SARPNET 76.64 % 85.63 % 71.31 % 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.
189 Anonymous 76.60 % 85.29 % 71.77 % 1 1 core @ 2.5 Ghz (Python)
190 DTFI 76.59 % 85.29 % 71.78 % 0.03 s 1 core @ 2.5 Ghz (Python)
191 CSNet8299 code 76.55 % 86.49 % 71.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
192 3D IoU Loss
This method makes use of Velodyne laser scans.
76.50 % 86.16 % 71.39 % 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.
193 F-ConvNet
This method makes use of Velodyne laser scans.
code 76.39 % 87.36 % 66.69 % 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.
194 variance_point 76.27 % 87.44 % 72.03 % 0.05 s 1 core @ 2.5 Ghz (Python)
195 SegVoxelNet 76.13 % 86.04 % 70.76 % 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.
196 S-AT GCN 76.04 % 83.20 % 71.17 % 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.
197 KPP3D code 76.00 % 86.66 % 71.07 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
198 TANet code 75.94 % 84.39 % 68.82 % 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.
199 CF-base-train 75.93 % 83.47 % 71.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
200 cp-tv-kp 75.85 % 83.50 % 72.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
201 PointRGCN 75.73 % 85.97 % 70.60 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
202 cp-tv 75.67 % 83.31 % 72.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
203 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 75.64 % 86.96 % 70.70 % 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.
204 Self-Calib Conv 75.59 % 83.54 % 71.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
205 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 75.43 % 86.10 % 68.88 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
206 CF-ctdep-train 75.43 % 83.03 % 71.31 % 1 s 1 core @ 2.5 Ghz (C/C++)
207 R-GCN 75.26 % 83.42 % 68.73 % 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.
208 epBRM
This method makes use of Velodyne laser scans.
code 75.15 % 85.00 % 69.84 % 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.
209 MAFF-Net(DAF-Pillar) 75.04 % 85.52 % 67.61 % 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.
210 T_PVRCNN 74.93 % 84.79 % 69.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
211 T_PVRCNN_V2 74.90 % 84.74 % 69.60 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
212 PI-RCNN 74.82 % 84.37 % 70.03 % 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.
213 MF 74.70 % 83.42 % 66.51 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
214 LazyTorch-CP-Infer-O 74.57 % 81.82 % 70.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
215 LazyTorch-CP-Small-P 74.44 % 81.73 % 70.14 % 1 s 1 core @ 2.5 Ghz (C/C++)
216 City-CF-fixed 74.37 % 83.23 % 69.65 % 1 s 1 core @ 2.5 Ghz (C/C++)
217 PointPillars
This method makes use of Velodyne laser scans.
code 74.31 % 82.58 % 68.99 % 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.
218 ARPNET 74.04 % 84.69 % 68.64 % 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.
219 CenterPoint (pcdet) 73.96 % 81.17 % 69.48 % 0.051 sec/frame 2 x V100
220 PC-CNN-V2
This method makes use of Velodyne laser scans.
73.79 % 85.57 % 65.65 % 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.
221 ZMMPP 73.78 % 82.48 % 68.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
222 C-GCN 73.62 % 83.49 % 67.01 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
223 3DBN
This method makes use of Velodyne laser scans.
73.53 % 83.77 % 66.23 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
224 Dune-DCF-e11 73.51 % 80.89 % 68.89 % 1 s 1 core @ 2.5 Ghz (C/C++)
225 CrazyTensor-CP 73.50 % 81.04 % 69.87 % 1 s 1 core @ 2.5 Ghz (Python)
226 PointRGBNet 73.49 % 83.99 % 68.56 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
227 City-CF 73.48 % 80.85 % 69.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
228 PSM_stereo 73.43 % 81.28 % 66.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
229 Dune-DCF-e15 73.29 % 80.34 % 68.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
230 SCNet
This method makes use of Velodyne laser scans.
73.17 % 83.34 % 67.93 % 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.
231 Dune-DCF-e09 73.15 % 80.40 % 68.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
232 AFTD 73.12 % 82.71 % 68.09 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
233 PP-PCdet code 73.07 % 83.32 % 68.18 % 0.01 s 1 core @ 2.5 Ghz (Python)
234 PFF3D
This method makes use of Velodyne laser scans.
code 72.93 % 81.11 % 67.24 % 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.
235 CrazyTensor-CF 72.92 % 79.87 % 68.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
236 DASS 72.31 % 81.85 % 65.99 % 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.
237 HS3D code 72.25 % 83.57 % 67.49 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
238 TBD_BD code 72.16 % 83.36 % 66.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
239 SSL_PP code 72.02 % 83.74 % 64.95 % 16ms GPU @ 1.5 Ghz (Python)
240 TBD 71.94 % 83.20 % 66.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
241 AVOD-FPN
This method makes use of Velodyne laser scans.
code 71.76 % 83.07 % 65.73 % 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.
242 PointPainting
This method makes use of Velodyne laser scans.
71.70 % 82.11 % 67.08 % 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.
243 Contrastive PP code 71.64 % 84.80 % 66.49 % 0.01 s 1 core @ 2.5 Ghz (Python)
244 CZY_3917 71.48 % 82.24 % 66.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
245 new_stereo 70.79 % 80.05 % 66.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
246 WS3D
This method makes use of Velodyne laser scans.
70.59 % 80.99 % 64.23 % 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.
247 F-PointNet
This method makes use of Velodyne laser scans.
code 69.79 % 82.19 % 60.59 % 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.
248 FusionDetv2-baseline 68.87 % 79.05 % 63.68 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
249 UberATG-ContFuse
This method makes use of Velodyne laser scans.
68.78 % 83.68 % 61.67 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
250 MLOD
This method makes use of Velodyne laser scans.
code 67.76 % 77.24 % 62.05 % 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.
251 DSGN++
This method uses stereo information.
code 67.37 % 83.21 % 59.91 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
252 DMF
This method uses stereo information.
67.33 % 77.55 % 62.44 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
253 Anonymous 66.97 % 83.77 % 58.41 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
254 AVOD
This method makes use of Velodyne laser scans.
code 66.47 % 76.39 % 60.23 % 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.
255 StereoDistill 66.39 % 81.66 % 57.39 % 0.4 s 1 core @ 2.5 Ghz (Python)
256 MMLAB LIGA-Stereo
This method uses stereo information.
code 64.66 % 81.39 % 57.22 % 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.
257 BirdNet+
This method makes use of Velodyne laser scans.
code 64.04 % 76.15 % 59.79 % 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.
258 CZY 63.68 % 77.56 % 57.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
259 MV3D
This method makes use of Velodyne laser scans.
63.63 % 74.97 % 54.00 % 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.
260 SD3DOD 62.00 % 76.09 % 55.46 % 0.04 s GPU @ 2.5 Ghz (Python)
261 AEC3D 61.99 % 72.16 % 57.11 % 18 ms GPU @ 2.5 Ghz (Python)
262 SNVC
This method uses stereo information.
code 61.34 % 78.54 % 54.23 % 1 s GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
263 RCD 60.56 % 70.54 % 55.58 % 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.
264 Anonymous 58.57 % 77.81 % 52.13 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
265 A3DODWTDA
This method makes use of Velodyne laser scans.
code 56.82 % 62.84 % 48.12 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
266 FD 56.40 % 73.05 % 52.25 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
267 PS++ code 55.28 % 74.80 % 46.70 % PS++ s 1 core @ 2.5 Ghz (C/C++)
268 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 54.88 % 68.38 % 49.16 % 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.
269 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
54.54 % 68.35 % 49.16 % 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.
270 CDN
This method uses stereo information.
code 54.22 % 74.52 % 46.36 % 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.
271 CG-Stereo
This method uses stereo information.
53.58 % 74.39 % 46.50 % 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.
272 PS code 52.88 % 74.41 % 44.38 % PS s 1 core @ 2.5 Ghz (C/C++)
273 UPF_3D
This method uses stereo information.
52.83 % 78.24 % 46.12 % 0.29 s 1 core @ 2.5 Ghz (Python)
274 DSGN
This method uses stereo information.
code 52.18 % 73.50 % 45.14 % 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.
275 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 51.85 % 70.14 % 50.03 % 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.
276 ppt 50.41 % 54.19 % 45.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
277 Complexer-YOLO
This method makes use of Velodyne laser scans.
47.34 % 55.93 % 42.60 % 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.
278 ESGN
This method uses stereo information.
46.39 % 65.80 % 38.42 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
279 Disp R-CNN (velo)
This method uses stereo information.
code 45.78 % 68.21 % 37.73 % 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.
280 CDN-PL++
This method uses stereo information.
44.86 % 64.31 % 38.11 % 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.
281 Disp R-CNN
This method uses stereo information.
code 43.27 % 67.02 % 36.43 % 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.
282 Pseudo-LiDAR++
This method uses stereo information.
code 42.43 % 61.11 % 36.99 % 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.
283 ART 42.42 % 63.38 % 36.44 % 20ms s 1 core @ 2.5 Ghz (C/C++)
284 YOLOStereo3D
This method uses stereo information.
code 41.25 % 65.68 % 30.42 % 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.
285 RT3D-GMP
This method uses stereo information.
38.76 % 45.79 % 30.00 % 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.
286 ZoomNet
This method uses stereo information.
code 38.64 % 55.98 % 30.97 % 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.
287 OC Stereo
This method uses stereo information.
code 37.60 % 55.15 % 30.25 % 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.
288 Pseudo-Lidar
This method uses stereo information.
code 34.05 % 54.53 % 28.25 % 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.
289 Stereo CenterNet
This method uses stereo information.
31.30 % 49.94 % 25.62 % 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.
290 Stereo R-CNN
This method uses stereo information.
code 30.23 % 47.58 % 23.72 % 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.
291 BirdNet
This method makes use of Velodyne laser scans.
27.26 % 40.99 % 25.32 % 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.
292 SparseLiDAR_fusion 26.23 % 36.85 % 21.45 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
293 VMDet_Boost 25.02 % 35.96 % 21.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
294 GCDR 23.92 % 34.89 % 19.59 % 0.28 s 1 core @ 2.5 Ghz (Python)
295 Anonymous 23.79 % 33.89 % 20.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
296 RT3DStereo
This method uses stereo information.
23.28 % 29.90 % 18.96 % 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.
297 Digging_M3D 21.24 % 29.15 % 19.18 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
298 VMDet 20.95 % 31.55 % 17.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
299 Anonymous 20.23 % 28.29 % 17.55 % 40 s 1 core @ 2.5 Ghz (C/C++)
300 SARM3D 19.70 % 25.20 % 17.35 % 0.03 s GPU @ 2.5 Ghz (Python)
301 RT3D
This method makes use of Velodyne laser scans.
19.14 % 23.74 % 18.86 % 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.
302 MonoInsight 19.04 % 27.71 % 16.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
303 CMKD* 18.69 % 28.55 % 16.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
304 Mix-Teaching 18.54 % 26.89 % 15.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
305 StereoFENet
This method uses stereo information.
18.41 % 29.14 % 14.20 % 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.
306 anonymity 18.00 % 28.10 % 15.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
307 SCSTSV-MonoFlex 17.91 % 27.38 % 15.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
308 MoGDE 17.88 % 27.07 % 15.66 % 0.03 s GPU @ 2.5 Ghz (Python)
309 LPCG-Monoflex 17.80 % 25.56 % 15.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
310 PS-fld code 17.74 % 23.74 % 15.14 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
311 DD3Dv2 code 17.61 % 26.36 % 15.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
312 MonoDDE 17.14 % 24.93 % 15.10 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
313 Anonymous 17.08 % 24.43 % 15.25 % 40 s 1 core @ 2.5 Ghz (C/C++)
314 OPA-3D code 17.05 % 24.60 % 14.25 % 0.04 s 1 core @ 3.5 Ghz (Python)
315 Mobile Stereo R-CNN
This method uses stereo information.
17.04 % 26.97 % 13.26 % 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.
316 anonymity 16.99 % 27.20 % 15.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
317 DD3D code 16.87 % 23.19 % 14.36 % 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) .
318 Shape-Aware 16.52 % 23.84 % 13.88 % 0.05 s 1 core @ 2.5 Ghz (Python)
319 MonoCon code 16.46 % 22.50 % 13.95 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
320 DID-M3D 16.29 % 24.40 % 13.75 % 0.04 s 1 core @ 2.5 Ghz (Python)
321 MonoDETR code 16.26 % 24.52 % 13.93 % 0.04 s 1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.
322 Lite-FPN-GUPNet 16.20 % 23.58 % 13.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
323 gupnet_se 16.10 % 23.62 % 13.41 % 0.03s 1 core @ 2.5 Ghz (C/C++)
324 zongmuDistill 16.08 % 25.11 % 13.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
325 MonoDistill 16.03 % 22.97 % 13.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
326 MDNet 16.01 % 24.59 % 13.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
327 DDS code 15.90 % 23.81 % 13.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
328 mono3d code 15.73 % 23.96 % 13.35 % TBD TBD
329 monopd code 15.72 % 23.51 % 13.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
330 OBMO_GUPNet 15.70 % 22.71 % 13.23 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
331 GPENet code 15.44 % 22.41 % 12.84 % 0.02 s GPU @ 2.5 Ghz (Python)
332 MonoDTR 15.39 % 21.99 % 12.73 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
333 mono3d 15.26 % 23.41 % 12.80 % 0.03 s GPU @ 2.5 Ghz (Python)
334 HBD 15.17 % 21.71 % 13.06 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
335 EW code 15.13 % 21.16 % 12.81 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
336 ZongmuMono3d code 15.08 % 23.79 % 13.25 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
337 GUPNet code 15.02 % 22.26 % 13.12 % 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.
338 HomoLoss(monoflex) code 14.94 % 21.75 % 13.07 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
339 Anonymous 14.87 % 23.93 % 12.45 % 40 s 1 core @ 2.5 Ghz (C/C++)
340 Anonymous 14.84 % 22.73 % 13.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
341 M3DSSD++ code 14.75 % 23.61 % 11.80 % 0.16s 1 core @ 2.5 Ghz (C/C++)
342 MonoFlex 14.73 % 22.29 % 12.77 % 0.03 s 1 core @ 2.5 Ghz (Python)
343 SGM3D 14.65 % 22.46 % 12.97 % 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.
344 Anonymous code 14.56 % 20.65 % 11.92 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
345 SAIC_ADC_Mono3D code 14.54 % 18.98 % 12.86 % 50 s GPU @ 2.5 Ghz (Python)
346 MonoEdge 14.47 % 21.08 % 12.73 % 0.05 s GPU @ 2.5 Ghz (Python)
347 MDSNet 14.46 % 24.30 % 11.12 % 0.07 s 1 core @ 2.5 Ghz (Python)
348 MonoGround 14.36 % 21.37 % 12.62 % 0.03 s 1 core @ 2.5 Ghz (Python)
349 MonoEdge-RCNN 14.35 % 19.74 % 11.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
350 ANM 14.33 % 20.84 % 11.61 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
351 DLE code 14.33 % 24.23 % 10.30 % 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.
352 SwinMono3D 14.24 % 22.61 % 10.11 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
353 AutoShape code 14.17 % 22.47 % 11.36 % 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.
354 MonoEdge-Rotate 14.13 % 21.60 % 12.27 % 0.05 s GPU @ 2.5 Ghz (Python)
355 EM code 14.00 % 22.93 % 11.26 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
356 MAOLoss code 14.00 % 20.05 % 11.81 % 0.05 s 1 core @ 2.5 Ghz (Python)
357 MonoFlex 13.89 % 19.94 % 12.07 % 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.
358 MonoEF 13.87 % 21.29 % 11.71 % 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.
359 MonoAug 13.85 % 20.06 % 11.43 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
360 DEPT 13.83 % 20.43 % 11.66 % 0.03 s 1 core @ 2.5 Ghz (Python)
361 K3D 13.80 % 20.04 % 11.67 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
362 none 13.79 % 18.84 % 11.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
363 DFR-Net 13.63 % 19.40 % 10.35 % 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.
364 MonoFar 13.52 % 18.08 % 11.58 % 0.04 s 1 core @ 2.5 Ghz (Python)
365 CaDDN code 13.41 % 19.17 % 11.46 % 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.
366 PCT code 13.37 % 21.00 % 11.31 % 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.
367 Ground-Aware code 13.25 % 21.65 % 9.91 % 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.
368 MK3D 13.19 % 20.48 % 11.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
369 Aug3D-RPN 12.99 % 17.82 % 9.78 % 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.
370 HomoLoss(imvoxelnet) code 12.99 % 20.10 % 10.50 % 0.20 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
371 DDMP-3D 12.78 % 19.71 % 9.80 % 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.
372 RetinaMono 12.73 % 19.41 % 10.45 % 0.02 s 1 core @ 2.5 Ghz (Python)
373 Kinematic3D code 12.72 % 19.07 % 9.17 % 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 .
374 M3DGAF 12.66 % 19.48 % 10.99 % 0.07 s 1 core @ 2.5 Ghz (Python)
375 MonoRCNN code 12.65 % 18.36 % 10.03 % 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.
376 MP-Mono 12.37 % 17.89 % 9.58 % 0.16 s GPU @ 2.5 Ghz (Python)
377 GrooMeD-NMS code 12.32 % 18.10 % 9.65 % 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.
378 MonoRUn code 12.30 % 19.65 % 10.58 % 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.
379 monodle code 12.26 % 17.23 % 10.29 % 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 .
380 LT-M3OD 12.26 % 18.15 % 10.05 % 0.03 s 1 core @ 2.5 Ghz (Python)
381 YoloMono3D code 12.06 % 18.28 % 8.42 % 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.
382 IAFA 12.01 % 17.81 % 10.61 % 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.
383 GAC3D 12.00 % 17.75 % 9.15 % 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.
384 CMAN 11.87 % 17.77 % 9.16 % 0.15 s 1 core @ 2.5 Ghz (Python)
385 PGD-FCOS3D code 11.76 % 19.05 % 9.39 % 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.
386 D4LCN code 11.72 % 16.65 % 9.51 % 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.
387 MonoAug 11.47 % 16.40 % 9.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
388 KM3D code 11.45 % 16.73 % 9.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
389 RefinedMPL 11.14 % 18.09 % 8.94 % 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.
390 PatchNet code 11.12 % 15.68 % 10.17 % 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.
391 ImVoxelNet code 10.97 % 17.15 % 9.15 % 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.
392 COF3D 10.91 % 17.86 % 8.20 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
393 MM 10.74 % 15.80 % 8.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
394 AM3D 10.74 % 16.50 % 9.52 % 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.
395 Lite-FPN 10.64 % 15.32 % 8.59 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
396 Keypoint-3D 10.42 % 15.97 % 7.91 % 14 s 1 core @ 2.5 Ghz (C/C++)
397 RTM3D code 10.34 % 14.41 % 8.77 % 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.
398 MonoPair 9.99 % 13.04 % 8.65 % 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.
399 Neighbor-Vote 9.90 % 15.57 % 8.89 % 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.
400 SMOKE code 9.76 % 14.03 % 7.84 % 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.
401 M3D-RPN code 9.71 % 14.76 % 7.42 % 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 .
402 QD-3DT
This is an online method (no batch processing).
code 9.33 % 12.81 % 7.86 % 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.
403 TopNet-HighRes
This method makes use of Velodyne laser scans.
9.28 % 12.67 % 7.95 % 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.
404 MonoCInIS 7.94 % 15.82 % 6.68 % 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.
405 SS3D 7.68 % 10.78 % 6.51 % 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.
406 MonoCInIS 7.66 % 15.21 % 6.24 % 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.
407 Mono3D_PLiDAR code 7.50 % 10.76 % 6.10 % 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.
408 MonoPSR code 7.25 % 10.76 % 5.85 % 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.
409 Decoupled-3D 7.02 % 11.08 % 5.63 % 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.
410 VoxelJones code 6.35 % 7.39 % 5.80 % .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.
411 MonoGRNet code 5.74 % 9.61 % 4.25 % 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.
412 A3DODWTDA (image) code 5.27 % 6.88 % 4.45 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
413 MonoFENet 5.14 % 8.35 % 4.10 % 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.
414 TLNet (Stereo)
This method uses stereo information.
code 4.37 % 7.64 % 3.74 % 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.
415 CSoR
This method makes use of Velodyne laser scans.
4.06 % 5.61 % 3.17 % 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.
416 Shift R-CNN (mono) code 3.87 % 6.88 % 2.83 % 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.
417 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 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.
418 SparVox3D 3.20 % 5.27 % 2.56 % 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.
419 TopNet-UncEst
This method makes use of Velodyne laser scans.
3.02 % 3.24 % 2.26 % 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.
420 GS3D 2.90 % 4.47 % 2.47 % 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.
421 3D-GCK 2.52 % 3.27 % 2.11 % 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.
422 WeakM3D code 2.26 % 5.03 % 1.63 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
423 ROI-10D 2.02 % 4.32 % 1.46 % 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.
424 CDTrack3D code 1.92 % 3.20 % 1.63 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
425 FQNet 1.51 % 2.77 % 1.01 % 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.
426 3D-SSMFCNN code 1.41 % 1.88 % 1.11 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
427 test code 0.03 % 0.01 % 0.03 % 50 s 1 core @ 2.5 Ghz (Python)
428 MonoDET code 0.01 % 0.03 % 0.01 % 0.04 s 1 core @ 2.5 Ghz (Python)
429 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 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.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 CasA++ 49.29 % 56.33 % 46.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 TED 49.21 % 55.85 % 46.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 VPFNet code 48.36 % 54.65 % 44.98 % 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.
4 CAD 47.91 % 55.98 % 44.63 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
5 DCAN-Second code 47.38 % 55.12 % 44.59 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
6 PV-RCNN++ code 47.19 % 54.29 % 43.49 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
7 CasA 47.09 % 54.04 % 44.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 EQ-PVRCNN code 47.02 % 55.84 % 42.94 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
9 PiFeNet 46.71 % 56.39 % 42.71 % 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.
10 ISE-RCNN 45.66 % 51.44 % 42.43 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
11 CAT-Det 45.44 % 54.26 % 41.94 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
12 HotSpotNet 45.37 % 53.10 % 41.47 % 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.
13 PE-RCVN 45.01 % 50.29 % 41.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
14 variance_point 44.89 % 53.72 % 41.21 % 0.05 s 1 core @ 2.5 Ghz (Python)
15 SPT 44.72 % 51.35 % 41.38 % 0.1 s GPU @ 2.5 Ghz (Python)
16 VPN 44.56 % 54.13 % 41.73 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
17 EPNet++ 44.38 % 52.79 % 41.29 % 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.
18 TANet code 44.34 % 53.72 % 40.49 % 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.
19 CFF-tv 44.33 % 52.72 % 41.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
20 3DSSD code 44.27 % 54.64 % 40.23 % 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.
21 AutoAlign 44.08 % 53.99 % 40.82 % 0.1 s 1 core @ 2.5 Ghz (Python)
22 ISE-RCNN-PV 43.78 % 50.03 % 40.50 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
23 Point-GNN
This method makes use of Velodyne laser scans.
code 43.77 % 51.92 % 40.14 % 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.
24 USVLab BSAODet 43.63 % 51.71 % 41.09 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
25 CFF-ep25 43.47 % 51.85 % 40.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
26 FV2P v2 43.47 % 50.64 % 40.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
27 F-ConvNet
This method makes use of Velodyne laser scans.
code 43.38 % 52.16 % 38.80 % 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.
28 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 43.35 % 53.10 % 40.06 % 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.
29 FS-Net
This method makes use of Velodyne laser scans.
43.31 % 49.82 % 40.89 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
30 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 43.29 % 52.17 % 40.29 % 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.
31 FromVoxelToPoint code 43.28 % 51.80 % 40.71 % 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.
32 VMVS
This method makes use of Velodyne laser scans.
43.27 % 53.44 % 39.51 % 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.
33 cff-tv-v2-ep25 43.25 % 51.40 % 40.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
34 P2V-RCNN 43.19 % 50.91 % 40.81 % 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.
35 KeyFuse2B 43.18 % 51.49 % 40.70 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
36 CF-ctdep-tv-ta 43.11 % 50.40 % 40.51 % 1 s 1 core @ 2.5 Ghz (C/C++)
37 MGAF-3DSSD code 43.09 % 50.65 % 39.65 % 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.
38 Reprod-Two-Branch 43.07 % 52.07 % 40.40 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
39 SGNet 43.00 % 49.68 % 40.45 % 0.09 s GPU @ 2.5 Ghz (Python)
40 Frustum-PointPillars code 42.89 % 51.22 % 39.28 % 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.
41 PSA-Det3D 42.81 % 49.72 % 39.58 % 0.1 s GPU @ 2.5 Ghz (Python)
42 CFF-tv-v2 42.77 % 51.08 % 40.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
43 Fast-CLOCs 42.72 % 52.10 % 39.08 % 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.
44 CF-base-tv 42.66 % 50.01 % 39.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
45 HMFI code 42.65 % 50.88 % 39.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
46 TBD 42.65 % 50.88 % 39.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 USVLab BSAODet (S) 42.62 % 49.52 % 39.12 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
48 STD code 42.47 % 53.29 % 38.35 % 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.
49 Anonymous 42.32 % 47.96 % 39.69 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
50 AVOD-FPN
This method makes use of Velodyne laser scans.
code 42.27 % 50.46 % 39.04 % 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.
51 SemanticVoxels 42.19 % 50.90 % 39.52 % 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.
52 TBD 42.19 % 49.89 % 39.34 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
53 TBD 42.19 % 49.89 % 39.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
54 F-PointNet
This method makes use of Velodyne laser scans.
code 42.15 % 50.53 % 38.08 % 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.
55 3SNet 42.02 % 48.91 % 39.39 % 0.07 s GPU @ 2.5 Ghz (Python)
56 CF-ctdep-tv 41.98 % 49.14 % 39.03 % 1 s 1 core @ 2.5 Ghz (C/C++)
57 Self-Calib Conv 41.95 % 48.88 % 39.52 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
58 PointPillars
This method makes use of Velodyne laser scans.
code 41.92 % 51.45 % 38.89 % 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.
59 CenterFuse 41.80 % 49.77 % 38.49 % 0.059 sec/frame 2 x V100
60 cp-tv-kp 41.70 % 48.77 % 39.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
61 epBRM
This method makes use of Velodyne laser scans.
code 41.52 % 49.17 % 39.08 % 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.
62 TCDVF 41.47 % 49.44 % 38.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 DGT-Det3D 41.40 % 49.06 % 38.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 tbd 41.10 % 50.56 % 37.49 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
65 IA-SSD (single) code 41.03 % 47.90 % 37.98 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
66 PointPainting
This method makes use of Velodyne laser scans.
40.97 % 50.32 % 37.87 % 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.
67 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 40.89 % 46.97 % 38.80 % 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.
68 SARFE 40.79 % 47.29 % 38.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
69 IoU-2B 40.62 % 50.33 % 36.94 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
70 MSADet 40.58 % 49.54 % 38.19 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
71 TBD 40.57 % 47.65 % 38.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 PDV code 40.56 % 47.80 % 38.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
73 cp-tv 40.55 % 47.71 % 38.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
74 MVMM code 40.49 % 47.54 % 38.36 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
75 cff-tv-t 40.41 % 49.46 % 37.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
76 SVGA-Net 40.39 % 48.48 % 37.92 % 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.
77 KeyPoint-IoUHead 40.15 % 47.86 % 37.01 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
78 TBD 40.07 % 46.11 % 37.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 M3DeTR code 39.94 % 45.70 % 37.66 % 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.
80 cp-tv-kp-io-sc 39.92 % 48.06 % 37.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
81 FusionDetv2-v5 39.91 % 47.50 % 37.39 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
82 DDet 39.87 % 45.82 % 38.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 CF-cd-io-tv 39.82 % 48.67 % 36.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
84 FusionDetv2-v4 39.68 % 46.93 % 37.31 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
85 DSASNet 39.65 % 47.14 % 37.05 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
86 VCRCNN 39.64 % 45.19 % 37.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
87 WGVRF 39.52 % 45.98 % 37.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 AFTD 39.45 % 48.28 % 36.07 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
89 TBD code 39.45 % 47.08 % 37.12 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
90 Dune-DCF-e09 39.43 % 47.29 % 36.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
91 LazyTorch-CP-Infer-O 39.43 % 47.38 % 36.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
92 SRDL 39.43 % 47.30 % 36.99 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
93 FusionDetv1 39.42 % 47.30 % 36.97 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
94 AGS-SSD[la] 39.41 % 46.04 % 36.28 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
95 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 39.37 % 47.98 % 36.01 % 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.
96 ST-RCNN
This method makes use of Velodyne laser scans.
39.36 % 44.96 % 37.09 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
97 LazyTorch-CP-Small-P 39.33 % 47.27 % 36.89 % 1 s 1 core @ 2.5 Ghz (C/C++)
98 ARPNET 39.31 % 48.32 % 35.93 % 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.
99 CenterPoint (pcdet) 39.28 % 47.25 % 36.78 % 0.051 sec/frame 2 x V100
100 CZY 39.26 % 45.08 % 36.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
101 Dune-DCF-e11 39.26 % 47.32 % 36.63 % 1 s 1 core @ 2.5 Ghz (C/C++)
102 ATT_SSD 39.24 % 45.92 % 36.35 % 0.01 s 1 core @ 2.5 Ghz (Python)
103 City-CF-fixed 39.22 % 47.68 % 36.55 % 1 s 1 core @ 2.5 Ghz (C/C++)
104 IA-SSD (multi) code 39.03 % 46.51 % 35.61 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
105 SIF 38.74 % 46.23 % 36.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
106 CrazyTensor-CP 38.67 % 46.58 % 36.15 % 1 s 1 core @ 2.5 Ghz (Python)
107 SCNet
This method makes use of Velodyne laser scans.
38.66 % 47.83 % 35.70 % 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.
108 Dune-DCF-e15 38.61 % 46.41 % 36.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
109 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 38.58 % 46.33 % 35.71 % 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. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
110 IKT3D
This method makes use of Velodyne laser scans.
38.58 % 44.18 % 36.56 % 0.05 s 1 core @ 2.5 Ghz (Python)
111 FPV-SSD 38.45 % 45.83 % 36.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
112 CF-base-train 38.44 % 45.89 % 35.55 % 1 s 1 core @ 2.5 Ghz (C/C++)
113 TBD 38.27 % 46.35 % 36.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
114 City-CF 38.04 % 45.42 % 35.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
115 CF-ctdep-train 38.03 % 44.75 % 35.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
116 NV-RCNN 37.82 % 44.38 % 35.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 PVTr 37.58 % 43.99 % 35.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
118 DVFENet 37.50 % 43.55 % 35.33 % 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.
119 MLOD
This method makes use of Velodyne laser scans.
code 37.47 % 47.58 % 35.07 % 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.
120 S-AT GCN 37.37 % 44.63 % 34.92 % 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.
121 T_PVRCNN 37.12 % 45.20 % 34.04 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
122 HS3D code 36.86 % 45.62 % 33.67 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
123 T_PVRCNN_V2 36.79 % 44.81 % 33.83 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
124 XView 36.79 % 42.44 % 34.96 % 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.
125 FusionDetv2-baseline 36.66 % 41.34 % 34.60 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
126 PFF3D
This method makes use of Velodyne laser scans.
code 36.07 % 43.93 % 32.86 % 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.
127 NV2P-RCNN 35.98 % 43.18 % 33.88 % 0.1 s GPU @ 2.5 Ghz (Python)
128 CrazyTensor-CF 35.83 % 43.50 % 33.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
129 BirdNet+
This method makes use of Velodyne laser scans.
code 35.06 % 41.55 % 32.93 % 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.
130 TBD_BD code 34.86 % 42.56 % 32.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
131 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 34.59 % 42.27 % 31.37 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
132 GT3D 33.56 % 42.96 % 29.93 % 0.1 s 1 core @ 2.5 Ghz (Python)
133 KPP3D code 32.91 % 41.34 % 30.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
134 DSGN++
This method uses stereo information.
code 32.74 % 43.05 % 29.54 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
135 ZMMPP 32.38 % 39.54 % 30.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
136 StereoDistill 32.23 % 44.12 % 28.95 % 0.4 s 1 core @ 2.5 Ghz (Python)
137 PP-PCdet code 32.04 % 39.23 % 29.79 % 0.01 s 1 core @ 2.5 Ghz (Python)
138 Contrastive PP code 31.64 % 38.47 % 29.30 % 0.01 s 1 core @ 2.5 Ghz (Python)
139 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 31.46 % 37.99 % 29.46 % 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.
140 CZY_3917 31.06 % 38.57 % 28.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
141 SparsePool code 30.38 % 37.84 % 26.94 % 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.
142 MMLAB LIGA-Stereo
This method uses stereo information.
code 30.00 % 40.46 % 27.07 % 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.
143 DMF
This method uses stereo information.
29.77 % 37.21 % 27.62 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
144 SparsePool code 27.92 % 35.52 % 25.87 % 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.
145 AVOD
This method makes use of Velodyne laser scans.
code 27.86 % 36.10 % 25.76 % 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.
146 PS++ code 27.45 % 36.89 % 24.01 % PS++ s 1 core @ 2.5 Ghz (C/C++)
147 CSW3D
This method makes use of Velodyne laser scans.
26.64 % 33.75 % 23.34 % 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.
148 PointRGBNet 26.40 % 34.77 % 24.03 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
149 PS code 26.01 % 35.52 % 23.24 % PS s 1 core @ 2.5 Ghz (C/C++)
150 Disp R-CNN (velo)
This method uses stereo information.
code 25.80 % 37.12 % 22.04 % 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.
151 CZY 25.47 % 32.33 % 23.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
152 Disp R-CNN
This method uses stereo information.
code 25.40 % 35.75 % 21.79 % 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.
153 CG-Stereo
This method uses stereo information.
24.31 % 33.22 % 20.95 % 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.
154 YOLOStereo3D
This method uses stereo information.
code 19.75 % 28.49 % 16.48 % 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.
155 AEC3D 19.00 % 24.39 % 17.43 % 18 ms GPU @ 2.5 Ghz (Python)
156 OC Stereo
This method uses stereo information.
code 17.58 % 24.48 % 15.60 % 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.
157 BirdNet
This method makes use of Velodyne laser scans.
17.08 % 22.04 % 15.82 % 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.
158 DSGN
This method uses stereo information.
code 15.55 % 20.53 % 14.15 % 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.
159 Complexer-YOLO
This method makes use of Velodyne laser scans.
13.96 % 17.60 % 12.70 % 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.
160 Anonymous 11.69 % 17.79 % 10.09 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
161 RT3D-GMP
This method uses stereo information.
11.41 % 16.23 % 10.12 % 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.
162 DD3D code 11.04 % 16.64 % 9.38 % 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) .
163 DD3Dv2 code 10.82 % 16.25 % 9.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
164 PS-fld code 10.82 % 16.95 % 9.26 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
165 DEPT 10.81 % 16.28 % 9.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
166 OPA-3D code 10.49 % 15.65 % 8.80 % 0.04 s 1 core @ 3.5 Ghz (Python)
167 anonymity 10.39 % 16.89 % 9.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
168 ESGN
This method uses stereo information.
10.27 % 14.05 % 9.02 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
169 MonoDTR 10.18 % 15.33 % 8.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
170 LT-M3OD 9.99 % 14.85 % 8.52 % 0.03 s 1 core @ 2.5 Ghz (Python)
171 GUPNet code 9.76 % 14.95 % 8.41 % 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.
172 MonoInsight 9.42 % 14.41 % 7.96 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
173 GPENet code 9.36 % 14.61 % 7.91 % 0.02 s GPU @ 2.5 Ghz (Python)
174 Lite-FPN-GUPNet 9.32 % 14.13 % 7.93 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
175 mono3d code 9.20 % 14.53 % 7.82 % TBD TBD
176 ZongmuMono3d code 9.18 % 14.23 % 7.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
177 SGM3D 8.81 % 13.99 % 7.26 % 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.
178 CMKD* 8.79 % 13.94 % 7.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
179 SCSTSV-MonoFlex 8.75 % 13.10 % 7.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
180 gupnet_se 8.65 % 13.40 % 7.78 % 0.03s 1 core @ 2.5 Ghz (C/C++)
181 SwinMono3D 8.54 % 12.96 % 7.19 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
182 SARM3D 8.48 % 12.95 % 7.25 % 0.03 s GPU @ 2.5 Ghz (Python)
183 MonoCon code 8.41 % 13.10 % 6.94 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
184 HBD 8.33 % 13.47 % 6.99 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
185 MonoEdge 8.33 % 12.11 % 7.03 % 0.05 s GPU @ 2.5 Ghz (Python)
186 GCDR 8.27 % 11.50 % 7.37 % 0.28 s 1 core @ 2.5 Ghz (Python)
187 MonoFlex 8.16 % 11.89 % 6.81 % 0.03 s 1 core @ 2.5 Ghz (Python)
188 CaDDN code 8.14 % 12.87 % 6.76 % 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.
189 MonoGround 7.89 % 12.37 % 7.13 % 0.03 s 1 core @ 2.5 Ghz (Python)
190 HomoLoss(monoflex) code 7.66 % 11.87 % 6.82 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
191 MonoFar 7.62 % 11.21 % 6.47 % 0.04 s 1 core @ 2.5 Ghz (Python)
192 K3D 7.60 % 12.58 % 6.73 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
193 ANM 7.54 % 11.92 % 6.37 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
194 SAIC_ADC_Mono3D code 7.54 % 12.06 % 6.41 % 50 s GPU @ 2.5 Ghz (Python)
195 Mix-Teaching 7.47 % 11.67 % 6.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
196 M3DGAF 7.42 % 11.68 % 6.65 % 0.07 s 1 core @ 2.5 Ghz (Python)
197 LPCG-Monoflex 7.33 % 10.82 % 6.18 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
198 MonoDDE 7.32 % 11.13 % 6.67 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
199 MonoAug 7.31 % 11.31 % 6.12 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
200 RefinedMPL 7.18 % 11.14 % 5.84 % 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.
201 Shape-Aware 7.16 % 10.40 % 5.93 % 0.05 s 1 core @ 2.5 Ghz (Python)
202 MDSNet 7.09 % 10.68 % 6.06 % 0.07 s 1 core @ 2.5 Ghz (Python)
203 MonoEdge-Rotate 7.02 % 10.47 % 5.84 % 0.05 s GPU @ 2.5 Ghz (Python)
204 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.92 % 10.40 % 6.63 % 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.
205 MonoRUn code 6.78 % 10.88 % 5.83 % 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.
206 DCD code 6.73 % 10.37 % 6.28 % 1 s 1 core @ 2.5 Ghz (C/C++)
207 MonoPair 6.68 % 10.02 % 5.53 % 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.
208 mono3d 6.62 % 10.10 % 5.46 % 0.03 s GPU @ 2.5 Ghz (Python)
209 monodle code 6.55 % 9.64 % 5.44 % 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 .
210 MonoAug 6.36 % 9.59 % 5.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
211 MonoFlex 6.31 % 9.43 % 5.26 % 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.
212 MDNet 5.66 % 8.24 % 4.74 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
213 M3DSSD++ code 5.65 % 8.10 % 4.72 % 0.16s 1 core @ 2.5 Ghz (C/C++)
214 MK3D 5.00 % 7.29 % 4.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
215 Aug3D-RPN 4.71 % 6.01 % 3.87 % 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.
216 MM 4.70 % 7.81 % 4.01 % 1 s 1 core @ 2.5 Ghz (C/C++)
217 Shift R-CNN (mono) code 4.66 % 7.95 % 4.16 % 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.
218 Lite-FPN 4.38 % 6.57 % 3.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
219 COF3D 4.37 % 6.02 % 3.55 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
220 MAOLoss code 4.18 % 5.81 % 3.67 % 0.05 s 1 core @ 2.5 Ghz (Python)
221 MonoPSR code 4.00 % 6.12 % 3.30 % 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.
222 MP-Mono 3.75 % 5.09 % 3.50 % 0.16 s GPU @ 2.5 Ghz (Python)
223 DFR-Net 3.62 % 6.09 % 3.39 % 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.
224 DDMP-3D 3.55 % 4.93 % 3.01 % 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.
225 M3D-RPN code 3.48 % 4.92 % 2.94 % 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 .
226 D4LCN code 3.42 % 4.55 % 2.83 % 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.
227 MoGDE 3.42 % 5.57 % 2.73 % 0.03 s GPU @ 2.5 Ghz (Python)
228 CMAN 3.41 % 4.62 % 2.87 % 0.15 s 1 core @ 2.5 Ghz (Python)
229 QD-3DT
This is an online method (no batch processing).
code 3.37 % 5.53 % 3.02 % 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.
230 MonoEF 2.79 % 4.27 % 2.21 % 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.
231 RT3DStereo
This method uses stereo information.
2.45 % 3.28 % 2.35 % 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.
232 TopNet-UncEst
This method makes use of Velodyne laser scans.
1.87 % 3.42 % 1.73 % 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.
233 SS3D 1.78 % 2.31 % 1.48 % 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.
234 PGD-FCOS3D code 1.49 % 2.28 % 1.38 % 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.
235 SparVox3D 1.35 % 1.93 % 1.04 % 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.
236 EM code 1.18 % 1.09 % 0.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
237 CDTrack3D code 1.01 % 1.48 % 0.69 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
238 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 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.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 TED 74.12 % 88.82 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 CasA++ 73.79 % 87.76 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 CasA 73.47 % 87.91 % 66.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 ISE-RCNN-PV 71.94 % 84.94 % 64.09 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
5 SGNet 70.40 % 86.75 % 62.73 % 0.09 s GPU @ 2.5 Ghz (Python)
6 HMFI code 70.37 % 84.02 % 62.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 CAD 69.94 % 84.68 % 62.21 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
8 SARFE 69.67 % 84.88 % 62.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
9 ISE-RCNN 69.18 % 82.62 % 62.77 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
10 EQ-PVRCNN code 69.10 % 85.41 % 62.30 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
11 Anonymous 69.00 % 82.74 % 62.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
12 CAT-Det 68.81 % 83.68 % 61.45 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
13 BtcDet
This method makes use of Velodyne laser scans.
code 68.68 % 82.81 % 61.81 % 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.
14 SPT 68.60 % 84.90 % 61.69 % 0.1 s GPU @ 2.5 Ghz (Python)
15 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 68.54 % 82.19 % 61.33 % 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.
68.35 % 81.81 % 60.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
17 PE-RCVN 68.13 % 84.96 % 60.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
18 PDV code 67.81 % 83.04 % 60.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
19 USVLab BSAODet 67.79 % 82.65 % 60.26 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
20 RangeIoUDet
This method makes use of Velodyne laser scans.
67.77 % 83.12 % 60.26 % 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.
21 DDet 67.55 % 82.03 % 60.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 3SNet 67.52 % 81.09 % 60.34 % 0.07 s GPU @ 2.5 Ghz (Python)
23 DCAN-Second code 67.50 % 84.90 % 60.78 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
24 PV-RCNN++ code 67.33 % 82.22 % 60.04 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
25 USVLab BSAODet (S) 67.25 % 81.94 % 59.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
26 CF-ctdep-tv-ta 67.08 % 85.49 % 59.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
27 SPG_mini
This method makes use of Velodyne laser scans.
code 66.96 % 80.21 % 60.50 % 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.
28 IKT3D
This method makes use of Velodyne laser scans.
66.87 % 80.35 % 60.14 % 0.05 s 1 core @ 2.5 Ghz (Python)
29 VCRCNN 66.78 % 81.29 % 59.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
30 M3DeTR code 66.74 % 83.83 % 59.03 % 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.
31 DSASNet 66.71 % 81.82 % 59.37 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
32 MSADet 66.49 % 84.21 % 59.73 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
33 FV2P v2 66.38 % 83.53 % 59.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
34 Reprod-Two-Branch 66.28 % 82.71 % 58.88 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
35 CFF-tv 66.26 % 82.09 % 58.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
36 IA-SSD (single) code 66.25 % 82.36 % 59.70 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
37 cff-tv-v2-ep25 66.14 % 82.40 % 58.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
38 CenterFuse 66.10 % 85.19 % 58.95 % 0.059 sec/frame 2 x V100
39 CFF-ep25 65.99 % 81.91 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
40 CZY 65.97 % 82.86 % 58.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
41 HotSpotNet 65.95 % 82.59 % 59.00 % 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.
42 ST-RCNN
This method makes use of Velodyne laser scans.
65.61 % 78.82 % 58.44 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
43 TBD 65.48 % 79.90 % 57.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
44 CFF-tv-v2 65.47 % 81.67 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
45 CF-ctdep-tv 65.43 % 82.29 % 57.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
46 JPVNet 65.41 % 80.66 % 59.26 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
47 Fast-CLOCs 65.31 % 82.83 % 57.43 % 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.
48 TCDVF 65.19 % 79.41 % 58.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 TBD 65.13 % 83.80 % 58.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 F-ConvNet
This method makes use of Velodyne laser scans.
code 65.07 % 81.98 % 56.54 % 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.
51 TBD 64.92 % 76.57 % 58.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 MVMM code 64.81 % 77.82 % 58.79 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
53 PVTr 64.51 % 81.09 % 57.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
54 FPV-SSD 64.40 % 78.36 % 56.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
55 DGT-Det3D 64.38 % 78.27 % 57.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
56 AutoAlign 64.36 % 80.41 % 56.88 % 0.1 s 1 core @ 2.5 Ghz (Python)
57 FusionDetv2-v5 64.28 % 78.57 % 57.02 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
58 cff-tv-t 64.16 % 83.46 % 57.21 % 1 s 1 core @ 2.5 Ghz (C/C++)
59 TBD code 64.12 % 77.56 % 57.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
60 TBD 64.12 % 79.27 % 57.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
61 KeyFuse2B 64.10 % 82.28 % 57.19 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
62 3DSSD code 64.10 % 82.48 % 56.90 % 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.
63 VPFNet code 64.10 % 77.64 % 58.00 % 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.
64 CF-base-tv 63.97 % 79.52 % 56.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
65 PointPainting
This method makes use of Velodyne laser scans.
63.78 % 77.63 % 55.89 % 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.
66 IoU-2B 63.75 % 82.21 % 56.43 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
67 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 63.71 % 78.60 % 57.65 % 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.
68 CF-cd-io-tv 63.69 % 82.60 % 56.66 % 1 s 1 core @ 2.5 Ghz (C/C++)
69 KeyPoint-IoUHead 63.65 % 81.44 % 56.94 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
70 WGVRF 63.58 % 78.81 % 57.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 NV-RCNN 63.57 % 80.12 % 56.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 63.52 % 79.17 % 56.93 % 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.
73 Point-GNN
This method makes use of Velodyne laser scans.
code 63.48 % 78.60 % 57.08 % 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.
74 MGAF-3DSSD code 63.43 % 80.64 % 55.15 % 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.
75 FromVoxelToPoint code 63.41 % 81.49 % 56.40 % 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.
76 FusionDetv2-v4 63.38 % 79.65 % 56.61 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
77 P2V-RCNN 63.13 % 78.62 % 56.81 % 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.
78 cp-tv-kp-io-sc 63.03 % 79.30 % 56.66 % 1 s 1 core @ 2.5 Ghz (C/C++)
79 H^23D R-CNN code 62.74 % 78.67 % 55.78 % 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.
80 SVGA-Net 62.28 % 78.58 % 54.88 % 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 ATT_SSD 62.07 % 76.56 % 55.87 % 0.01 s 1 core @ 2.5 Ghz (Python)
82 SRDL 62.02 % 77.35 % 55.52 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
83 FusionDetv1 62.02 % 77.33 % 55.52 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
84 cp-tv 62.01 % 77.26 % 55.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
85 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 62.00 % 77.36 % 55.40 % 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. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
86 DVFENet 62.00 % 78.73 % 55.18 % 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.
87 NV2P-RCNN 61.95 % 73.58 % 55.62 % 0.1 s GPU @ 2.5 Ghz (Python)
88 IA-SSD (multi) code 61.94 % 78.35 % 55.70 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
89 KPP3D code 61.85 % 76.43 % 55.78 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
90 Self-Calib Conv 61.84 % 77.26 % 55.37 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
91 VPN 61.82 % 77.81 % 55.33 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
92 PSA-Det3D 61.79 % 75.82 % 55.12 % 0.1 s GPU @ 2.5 Ghz (Python)
93 S-AT GCN 61.70 % 75.24 % 55.32 % 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.
94 SIF 61.61 % 77.13 % 55.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
95 STD code 61.59 % 78.69 % 55.30 % 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.
96 Dune-DCF-e11 61.03 % 80.38 % 54.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
97 City-CF 60.84 % 79.32 % 53.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
98 Dune-DCF-e15 60.53 % 78.68 % 53.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
99 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 60.30 % 75.42 % 53.81 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
100 EPNet++ 59.71 % 76.15 % 53.67 % 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.
101 cp-tv-kp 59.68 % 75.54 % 53.34 % 1 s 1 core @ 2.5 Ghz (C/C++)
102 City-CF-fixed 59.56 % 77.39 % 53.32 % 1 s 1 core @ 2.5 Ghz (C/C++)
103 XView 59.55 % 77.24 % 53.47 % 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.
104 TANet code 59.44 % 75.70 % 52.53 % 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.
105 TBD_BD code 59.42 % 77.20 % 53.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
106 CF-ctdep-train 59.35 % 77.73 % 52.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
107 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 58.82 % 74.96 % 52.53 % 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.
108 CF-base-train 58.80 % 76.64 % 51.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
109 CrazyTensor-CF 58.72 % 78.24 % 51.39 % 1 s 1 core @ 2.5 Ghz (C/C++)
110 HS3D code 58.65 % 74.75 % 52.98 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
111 PointPillars
This method makes use of Velodyne laser scans.
code 58.65 % 77.10 % 51.92 % 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.
112 variance_point 58.45 % 75.33 % 51.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
113 AGS-SSD[la] 58.35 % 72.99 % 52.76 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
114 ARPNET 58.20 % 74.21 % 52.13 % 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.
115 ZMMPP 58.03 % 71.72 % 51.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
116 Dune-DCF-e09 57.82 % 74.49 % 51.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
117 AFTD 57.44 % 75.50 % 51.12 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
118 LazyTorch-CP-Small-P 56.82 % 73.06 % 50.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
119 LazyTorch-CP-Infer-O 56.77 % 73.03 % 50.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
120 CZY_3917 56.69 % 71.06 % 50.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
121 CenterPoint (pcdet) 56.67 % 73.04 % 50.60 % 0.051 sec/frame 2 x V100
122 FusionDetv2-baseline 56.34 % 71.16 % 50.70 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
123 T_PVRCNN 56.26 % 70.51 % 49.90 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
124 Contrastive PP code 56.24 % 71.38 % 49.15 % 0.01 s 1 core @ 2.5 Ghz (Python)
125 epBRM
This method makes use of Velodyne laser scans.
code 56.13 % 72.08 % 49.91 % 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.
126 F-PointNet
This method makes use of Velodyne laser scans.
code 56.12 % 72.27 % 49.01 % 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.
127 CrazyTensor-CP 55.31 % 72.10 % 49.40 % 1 s 1 core @ 2.5 Ghz (Python)
128 T_PVRCNN_V2 55.29 % 69.58 % 49.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
129 PP-PCdet code 54.25 % 68.87 % 48.06 % 0.01 s 1 core @ 2.5 Ghz (Python)
130 GT3D 54.08 % 73.71 % 47.95 % 0.1 s 1 core @ 2.5 Ghz (Python)
131 TBD 53.95 % 70.44 % 47.22 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
132 TBD 53.95 % 70.44 % 47.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
133 BirdNet+
This method makes use of Velodyne laser scans.
code 53.84 % 65.67 % 49.06 % 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.
134 tbd 53.00 % 68.71 % 46.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
135 PointRGBNet 52.15 % 67.05 % 46.78 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
136 DMF
This method uses stereo information.
51.33 % 65.51 % 45.05 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
137 PiFeNet 51.10 % 67.50 % 44.66 % 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.
138 SCNet
This method makes use of Velodyne laser scans.
50.79 % 67.98 % 45.15 % 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.
139 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.55 % 63.76 % 44.93 % 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.
140 MLOD
This method makes use of Velodyne laser scans.
code 49.43 % 68.81 % 42.84 % 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.
141 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 47.72 % 67.38 % 42.89 % 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.
142 PFF3D
This method makes use of Velodyne laser scans.
code 46.78 % 63.27 % 41.37 % 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.
143 CZY 45.32 % 59.97 % 40.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
144 StereoDistill 44.02 % 63.96 % 39.19 % 0.4 s 1 core @ 2.5 Ghz (Python)
145 DSGN++
This method uses stereo information.
code 43.90 % 62.82 % 39.21 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
146 AVOD
This method makes use of Velodyne laser scans.
code 42.08 % 57.19 % 38.29 % 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.
147 SparsePool code 37.33 % 52.61 % 33.39 % 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.
148 MMLAB LIGA-Stereo
This method uses stereo information.
code 36.86 % 54.44 % 32.06 % 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.
149 SparsePool code 32.61 % 40.87 % 29.05 % 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.
150 CG-Stereo
This method uses stereo information.
30.89 % 47.40 % 27.23 % 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.
151 PS++ code 30.75 % 47.77 % 26.67 % PS++ s 1 core @ 2.5 Ghz (C/C++)
152 BirdNet
This method makes use of Velodyne laser scans.
30.25 % 43.98 % 27.21 % 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.
153 PS code 26.77 % 41.22 % 23.76 % PS s 1 core @ 2.5 Ghz (C/C++)
154 Disp R-CNN (velo)
This method uses stereo information.
code 24.40 % 40.05 % 21.12 % 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.
155 Disp R-CNN
This method uses stereo information.
code 24.40 % 40.04 % 21.12 % 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.
156 AEC3D 22.41 % 31.40 % 21.56 % 18 ms GPU @ 2.5 Ghz (Python)
157 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.53 % 24.27 % 17.31 % 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.
158 DSGN
This method uses stereo information.
code 18.17 % 27.76 % 16.21 % 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.
159 OC Stereo
This method uses stereo information.
code 16.63 % 29.40 % 14.72 % 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.
160 RT3D-GMP
This method uses stereo information.
12.99 % 18.31 % 10.63 % 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.
161 ESGN
This method uses stereo information.
7.69 % 13.84 % 6.75 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
162 CMKD* 6.67 % 12.52 % 6.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
163 PS-fld code 6.18 % 11.22 % 5.21 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
164 DD3Dv2 code 5.68 % 8.79 % 4.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
165 anonymity 5.68 % 9.27 % 4.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
166 mono3d code 5.27 % 10.08 % 4.12 % TBD TBD
167 Anonymous 5.24 % 9.60 % 4.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
168 anonymity 5.22 % 9.08 % 4.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
169 Mix-Teaching 4.91 % 8.04 % 4.15 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
170 DD3D code 4.79 % 7.52 % 4.22 % 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) .
171 MonoPSR code 4.74 % 8.37 % 3.68 % 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.
172 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.54 % 7.13 % 3.81 % 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.
173 LT-M3OD 4.52 % 7.87 % 4.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
174 LPCG-Monoflex 4.38 % 6.98 % 3.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
175 DEPT 4.29 % 7.67 % 3.33 % 0.03 s 1 core @ 2.5 Ghz (Python)
176 Lite-FPN-GUPNet 4.19 % 6.22 % 3.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
177 MAOLoss code 4.06 % 6.71 % 3.16 % 0.05 s 1 core @ 2.5 Ghz (Python)
178 MonoInsight 3.92 % 6.23 % 3.27 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
179 MDNet 3.88 % 6.93 % 3.10 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
180 SCSTSV-MonoFlex 3.82 % 6.65 % 3.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
181 SAIC_ADC_Mono3D code 3.81 % 6.73 % 3.03 % 50 s GPU @ 2.5 Ghz (Python)
182 Shape-Aware 3.78 % 6.26 % 3.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
183 MonoDDE 3.78 % 5.94 % 3.33 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
184 ZongmuMono3d code 3.77 % 7.21 % 3.15 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
185 DFR-Net 3.58 % 5.69 % 3.10 % 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.
186 HomoLoss(monoflex) code 3.50 % 5.48 % 2.99 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
187 OPA-3D code 3.45 % 5.16 % 2.86 % 0.04 s 1 core @ 3.5 Ghz (Python)
188 CaDDN code 3.41 % 7.00 % 3.30 % 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.
189 SARM3D 3.37 % 4.93 % 2.95 % 0.03 s GPU @ 2.5 Ghz (Python)
190 RT3DStereo
This method uses stereo information.
3.37 % 5.29 % 2.57 % 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.
191 MonoDTR 3.27 % 5.05 % 3.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
192 GUPNet code 3.21 % 5.58 % 2.66 % 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.
193 MonoAug 3.15 % 5.22 % 2.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
194 GPENet code 3.05 % 5.45 % 2.56 % 0.02 s GPU @ 2.5 Ghz (Python)
195 MoGDE 2.94 % 5.08 % 2.74 % 0.03 s GPU @ 2.5 Ghz (Python)
196 M3DSSD++ code 2.94 % 5.18 % 2.43 % 0.16s 1 core @ 2.5 Ghz (C/C++)
197 SGM3D 2.92 % 5.49 % 2.64 % 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.
198 K3D 2.81 % 5.17 % 2.57 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
199 MonoFar 2.75 % 4.46 % 2.64 % 0.04 s 1 core @ 2.5 Ghz (Python)
200 DCD code 2.74 % 4.72 % 2.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
201 ANM 2.69 % 4.69 % 2.68 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
202 MonoGround 2.68 % 4.62 % 2.53 % 0.03 s 1 core @ 2.5 Ghz (Python)
203 MDSNet 2.68 % 5.37 % 2.22 % 0.07 s 1 core @ 2.5 Ghz (Python)
204 monodle code 2.66 % 4.59 % 2.45 % 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 .
205 SwinMono3D 2.54 % 3.76 % 2.31 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
206 mono3d 2.53 % 4.71 % 2.22 % 0.03 s GPU @ 2.5 Ghz (Python)
207 MonoEdge-Rotate 2.51 % 4.28 % 2.13 % 0.05 s GPU @ 2.5 Ghz (Python)
208 DDMP-3D 2.50 % 4.18 % 2.32 % 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.
209 Aug3D-RPN 2.43 % 4.36 % 2.55 % 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.
210 QD-3DT
This is an online method (no batch processing).
code 2.39 % 4.16 % 1.85 % 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.
211 MonoFlex 2.35 % 4.17 % 2.04 % 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.
212 M3DGAF 2.30 % 4.28 % 2.12 % 0.07 s 1 core @ 2.5 Ghz (Python)
213 MonoAug 2.23 % 3.68 % 1.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
214 MonoEdge 2.19 % 3.15 % 1.77 % 0.05 s GPU @ 2.5 Ghz (Python)
215 gupnet_se 2.13 % 3.84 % 2.13 % 0.03s 1 core @ 2.5 Ghz (C/C++)
216 MonoPair 2.12 % 3.79 % 1.83 % 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.
217 MonoFlex 2.10 % 3.39 % 1.67 % 0.03 s 1 core @ 2.5 Ghz (Python)
218 MK3D 2.02 % 3.75 % 1.96 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
219 MonoCon code 1.92 % 2.80 % 1.55 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
220 RefinedMPL 1.82 % 3.23 % 1.77 % 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.
221 TopNet-HighRes
This method makes use of Velodyne laser scans.
1.67 % 2.49 % 1.88 % 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.
222 D4LCN code 1.67 % 2.45 % 1.36 % 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.
223 MP-Mono 1.58 % 2.36 % 1.69 % 0.16 s GPU @ 2.5 Ghz (Python)
224 COF3D 1.46 % 2.34 % 1.28 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
225 SS3D 1.45 % 2.80 % 1.35 % 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.
226 PGD-FCOS3D code 1.38 % 2.81 % 1.20 % 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.
227 HBD 1.24 % 2.45 % 1.22 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
228 GCDR 1.17 % 1.96 % 1.02 % 0.28 s 1 core @ 2.5 Ghz (Python)
229 CMAN 1.05 % 1.59 % 1.11 % 0.15 s 1 core @ 2.5 Ghz (Python)
230 MonoEF 0.92 % 1.80 % 0.71 % 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.
231 M3D-RPN code 0.65 % 0.94 % 0.47 % 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 MonoRUn code 0.61 % 1.01 % 0.48 % 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.
233 Lite-FPN 0.41 % 0.50 % 0.24 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
234 Shift R-CNN (mono) code 0.29 % 0.48 % 0.31 % 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.
235 MM 0.27 % 0.48 % 0.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
236 CDTrack3D code 0.06 % 0.06 % 0.07 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
237 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 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.
Table as LaTeX | Only published Methods

Related Datasets

Citation

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}



eXTReMe Tracker