Object Detection Evaluation 2012


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

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

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

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

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 CLOCs_PVCas 95.96 % 96.76 % 91.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
2 SPANet 95.46 % 96.54 % 90.47 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
3 PC-CNN-V2
This method makes use of Velodyne laser scans.
95.20 % 96.06 % 89.37 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
4 HUAWEI Octopus-LIS 95.18 % 96.06 % 92.61 % 0.1 s 1 core @ 2.5 Ghz (Python)
5 F-PointNet
This method makes use of Velodyne laser scans.
code 95.17 % 95.85 % 85.42 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
6 SA-SSD code 95.16 % 97.92 % 90.15 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
7 3DSSD code 95.10 % 97.69 % 92.18 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
8 MVRA + I-FRCNN+ 94.98 % 95.87 % 82.52 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
9 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 94.70 % 98.17 % 92.04 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
10 D3D 94.66 % 95.43 % 89.72 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
11 nonet 94.62 % 95.86 % 91.86 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
12 CN 94.60 % 97.86 % 89.81 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
13 BM-NET 94.49 % 95.09 % 85.06 % 0.5 s GPU @ 2.5 Ghz (Python + C/C++)
14 TuSimple code 94.47 % 95.12 % 86.45 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
15 EPNet code 94.44 % 96.15 % 89.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
16 SERCNN
This method makes use of Velodyne laser scans.
94.42 % 96.33 % 89.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
17 CVRS_PF 94.37 % 95.56 % 91.43 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
18 OneCoLab SicNet V2 94.29 % 95.45 % 91.87 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
19 UberATG-MMF
This method makes use of Velodyne laser scans.
94.25 % 97.41 % 89.87 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
20 FDet 94.23 % 95.84 % 91.80 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
21 tbd code 94.21 % 95.68 % 91.49 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
22 HR-faster-rcnn 94.14 % 95.41 % 86.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
23 OAP 93.93 % 96.85 % 86.37 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
24 HRI-MSP-L
This method makes use of Velodyne laser scans.
93.92 % 95.51 % 91.42 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
25 DGIST-CellBox 93.90 % 95.86 % 88.26 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
26 Associate-3Ddet_v2 93.77 % 96.83 % 88.57 % 0.04 s 1 core @ 2.5 Ghz (Python)
27 Patches - EMP
This method makes use of Velodyne laser scans.
93.75 % 97.91 % 90.56 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
28 VAL 93.71 % 96.92 % 83.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
29 CIA-SSD
This method makes use of Velodyne laser scans.
93.68 % 96.92 % 86.15 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
30 Noah CV Lab - SSL 93.65 % 94.02 % 86.02 % 0.1 s GPU @ 2.5 Ghz (Python)
31 MVX-Net++ 93.58 % 96.41 % 88.51 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
32 CLOCs_PointCas 93.55 % 96.69 % 86.16 % 0.1 s GPU @ 2.5 Ghz (Python)
33 MonoPair 93.55 % 96.61 % 83.55 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
34 Deep MANTA 93.50 % 98.89 % 83.21 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
35 Point-GNN
This method makes use of Velodyne laser scans.
code 93.50 % 96.58 % 88.35 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
36 AIMC-RUC 93.47 % 96.75 % 88.35 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
37 dgist_multiDetNet 93.46 % 94.99 % 85.46 % 0.08 s GPU Titanx Pascal (Python)
38 FichaDL 93.46 % 96.00 % 84.39 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
39 scssd-normal(0.3) 93.45 % 96.72 % 88.31 % 0.05 s GPU @ 2.5 Ghz (Python)
40 PC-RGNN 93.43 % 96.81 % 88.25 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
41 Cas-SSD 93.41 % 96.73 % 88.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 RRC code 93.40 % 95.68 % 87.37 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
43 KNN-GCNN 93.39 % 96.19 % 88.17 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
44 F-3DNet 93.38 % 96.51 % 88.32 % 0.5 s GPU @ 2.5 Ghz (Python)
45 3D-CVF at SPA
This method makes use of Velodyne laser scans.
93.36 % 96.78 % 86.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
46 FLID 93.35 % 95.90 % 85.69 % 0.04 s GPU @ 2.5 Ghz (Python)
47 scssd-normal(0.4) 93.31 % 96.59 % 88.23 % 0.05 s 1 core @ 2.5 Ghz (Python)
48 CFENet 93.26 % 93.91 % 86.99 % 4 s GPU @ 2.5 Ghz (Python + C/C++)
49 STD code 93.22 % 96.14 % 90.53 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
50 SARPNET 93.21 % 96.07 % 88.09 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
51 Fast Point R-CNN
This method makes use of Velodyne laser scans.
93.18 % 96.13 % 87.68 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
52 sensekitti code 93.17 % 94.79 % 84.38 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
53 ELE 93.14 % 98.44 % 90.32 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
54 RethinkDet3D 93.14 % 96.16 % 88.17 % 0.15 s 1 core @ 2.5 Ghz (Python)
55 Discrete-PointDet 93.14 % 96.36 % 87.82 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
56 SJTU-HW 93.11 % 96.30 % 82.21 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
57 PVF-NET 93.08 % 96.03 % 88.04 % 0.1 s 1 core @ 2.5 Ghz (Python)
58 SerialR-FCN+SG-NMS 93.03 % 95.81 % 83.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
59 PointRCNN-deprecated
This method makes use of Velodyne laser scans.
92.96 % 96.72 % 85.81 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
60 CLOCs_SecCas 92.95 % 95.43 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
61 cvMax 92.84 % 96.14 % 87.87 % 0.04 s GPU @ >3.5 Ghz (Python)
62 HotSpotNet 92.81 % 96.21 % 89.80 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
63 deprecated 92.79 % 96.12 % 87.78 % 0.04 s GPU @ 2.5 Ghz (Python)
64 PointCSE 92.78 % 95.99 % 87.66 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
65 IGRP 92.78 % 96.28 % 87.81 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
66 Mono3CN 92.76 % 95.51 % 84.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 MuRF 92.74 % 95.74 % 87.64 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
68 SegVoxelNet 92.73 % 96.00 % 87.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
69 Patches
This method makes use of Velodyne laser scans.
92.72 % 96.34 % 87.63 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
70 CenterNet3D 92.69 % 95.76 % 89.81 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
71 Chovy 92.69 % 96.06 % 89.74 % 0.04 s GPU @ 2.5 Ghz (Python)
72 PPFNet code 92.68 % 96.32 % 87.66 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
73 R-GCN 92.67 % 96.19 % 87.66 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
74 NLK-3D 92.67 % 95.44 % 87.72 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
75 PI-RCNN 92.66 % 96.17 % 87.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
76 92.65 % 96.09 % 89.72 %
77 deprecated 92.60 % 96.20 % 89.60 % - -
78 deprecated 92.59 % 96.21 % 89.58 % 0.05 s GPU @ >3.5 Ghz (Python)
79 PointPainting
This method makes use of Velodyne laser scans.
92.58 % 98.39 % 89.71 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
80 SPA 92.56 % 95.96 % 87.60 % 0.1 s 1 core @ 2.5 Ghz (Python)
81 DEFT 92.55 % 96.17 % 89.51 % 1 s GPU @ 2.5 Ghz (Python)
82 3D IoU-Net 92.47 % 96.31 % 87.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
83 PPBA 92.46 % 95.22 % 87.53 % NA s GPU @ 2.5 Ghz (Python)
84 TBU 92.46 % 95.22 % 87.53 % NA s GPU @ 2.5 Ghz (Python)
85 VAR 92.46 % 95.11 % 89.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
86 Associate-3Ddet code 92.45 % 95.61 % 87.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
87 CP
This method makes use of Velodyne laser scans.
92.44 % 96.14 % 87.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 92.39 % 95.84 % 89.51 %
89 OneCoLab SicNet 92.37 % 95.57 % 89.79 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
90 PointRGCN 92.33 % 97.51 % 87.07 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
91 LZY_RCNN 92.28 % 93.58 % 89.76 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
92 F-ConvNet
This method makes use of Velodyne laser scans.
code 92.19 % 95.85 % 80.09 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
93 MDA 92.17 % 94.88 % 89.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
94 yolo4 92.13 % 94.20 % 79.89 % 0.02 s 1 core @ 2.5 Ghz (Python)
95 TBD 92.12 % 93.48 % 89.56 % 0.05 s GPU @ 2.5 Ghz (Python)
96 PVNet 92.12 % 94.84 % 89.27 % 0,1 s 1 core @ 2.5 Ghz (Python)
97 IE-PointRCNN 92.08 % 96.01 % 87.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
98 PBASN code 92.07 % 95.51 % 87.04 % NA s GPU @ 2.5 Ghz (Python)
99 SDP+RPN 92.03 % 95.16 % 79.16 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
100 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 92.00 % 95.88 % 86.98 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
101 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 91.90 % 95.92 % 87.11 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
102 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 91.86 % 95.03 % 89.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
103 MBR-SSD 91.83 % 93.46 % 84.97 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
104 Pointpillar_TV 91.82 % 94.82 % 88.57 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
105 epBRM
This method makes use of Velodyne laser scans.
code 91.77 % 94.59 % 88.45 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
106 deprecated 91.76 % 96.53 % 83.90 % 0.05 s 1 core @ 2.5 Ghz (Python)
107 3DBN_2 91.75 % 95.34 % 89.12 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
108 C-GCN 91.73 % 95.64 % 86.37 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
109 ITVD code 91.73 % 95.85 % 79.31 % 0.3 s GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
110 PiP 91.67 % 94.35 % 88.35 % 0.033 s 1 core @ 2.5 Ghz (Python)
111 SINet+ code 91.67 % 94.17 % 78.60 % 0.3 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
112 Faster RCNN + A 91.60 % 94.77 % 81.43 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
113 Cascade MS-CNN code 91.60 % 94.26 % 78.84 % 0.25 s GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019.
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
114 deprecated 91.59 % 94.34 % 79.14 % 0.05 s GPU @ 2.0 Ghz (Python)
115 tt code 91.59 % 95.15 % 88.72 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
116 yolo4_5l 91.50 % 91.26 % 81.89 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
117 HRI-VoxelFPN 91.44 % 96.65 % 86.18 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
118 RUC 91.40 % 95.02 % 88.41 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
119 CU-PointRCNN 91.34 % 97.25 % 86.98 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
120 deprecated 91.31 % 96.90 % 83.91 % 0.06 s GPU @ >3.5 Ghz (Python)
121 Faster RCNN + G 91.28 % 94.34 % 81.02 % 1.1 s GPU @ 2.5 Ghz (Python)
122 Faster RCNN + Gr + A 91.25 % 94.09 % 81.25 % 1.29 s GPU @ 2.5 Ghz (Python)
123 OACV 91.21 % 94.23 % 83.07 % 0.23 s GPU @ 2.5 Ghz (Python)
124 CentrNet-v1
This method makes use of Velodyne laser scans.
91.21 % 94.22 % 88.36 % 0.03 s GPU @ 2.5 Ghz (Python)
125 CentrNet-FG 91.21 % 94.05 % 88.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
126 PointPillars
This method makes use of Velodyne laser scans.
code 91.19 % 94.00 % 88.17 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
127 Faster RCNN + A 91.19 % 94.43 % 80.99 % 0.19 s GPU @ 2.5 Ghz (Python)
128 LTN 91.18 % 94.68 % 81.51 % 0.4 s GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
129 autonet 91.17 % 93.70 % 88.10 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
130 WS3D
This method makes use of Velodyne laser scans.
91.15 % 95.13 % 86.52 % 0.1 s GPU @ 2.5 Ghz (Python)
131 PointPiallars_SECA 91.12 % 93.66 % 87.94 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
132 DDB
This method makes use of Velodyne laser scans.
91.12 % 93.71 % 87.34 % 0.05 s GPU @ 2.5 Ghz (Python)
133 EPENet 91.11 % 94.31 % 88.02 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
134 anonymous 91.08 % 96.57 % 82.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
135 SSL-RTM3D 91.07 % 96.44 % 81.19 % 0.03 s 1 core @ 2.5 Ghz (Python)
136 FII-CenterNet 91.03 % 94.48 % 83.00 % 0.09 s GPU @ 2.5 Ghz (Python)
137 Aston-EAS 91.02 % 93.91 % 77.93 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
138 ARPNET 90.99 % 94.00 % 83.49 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
139 Bit 90.96 % 93.84 % 87.47 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
140 JSU-NET 90.90 % 96.41 % 80.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
141 GAFM 90.90 % 96.46 % 80.70 % 0.5 s 1 core @ 2.5 Ghz (Python)
142 PatchNet 90.87 % 93.82 % 79.62 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
143 GA_BALANCE 90.86 % 96.19 % 78.40 % 1 s 1 core @ 2.5 Ghz (Python)
144 MV3D
This method makes use of Velodyne laser scans.
90.83 % 96.47 % 78.63 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
145 MVSLN 90.81 % 96.12 % 83.39 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
146 3D IoU Loss
This method makes use of Velodyne laser scans.
90.79 % 95.92 % 85.65 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
147 SINet_VGG code 90.79 % 93.59 % 77.53 % 0.2 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
148 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
90.74 % 93.80 % 86.75 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
149 GA_FULLDATA 90.73 % 96.31 % 78.22 % 1 s 4 cores @ 2.5 Ghz (Python)
150 Simple3D Net 90.70 % 93.54 % 87.81 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
151 HR-SECOND code 90.68 % 93.72 % 85.63 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
152 GA2500 90.68 % 95.86 % 80.29 % 0.2 s 1 core @ 2.5 Ghz (Python)
153 GA_rpn500 90.68 % 95.86 % 80.29 % 1 s 1 core @ 2.5 Ghz (Python)
154 TANet code 90.67 % 93.67 % 85.31 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
155 SFB-SECOND 90.67 % 96.17 % 85.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
156 SECOND-V1.5
This method makes use of Velodyne laser scans.
code 90.65 % 95.96 % 85.35 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
157 yolo4 90.63 % 94.71 % 80.38 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
158 P3D 90.62 % 94.31 % 84.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
159 baseline 90.59 % 93.29 % 87.18 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
160 VOXEL_FPN_HR 90.55 % 93.76 % 85.42 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
161 MP 90.50 % 93.86 % 85.17 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
162 Sogo_MM 90.46 % 94.31 % 80.62 % 1.5 s GPU @ 2.5 Ghz (C/C++)
163 bigger_ga 90.38 % 95.76 % 77.92 % 1 s 1 core @ 2.5 Ghz (Python)
164 CG-Stereo
This method uses stereo information.
90.38 % 96.31 % 82.80 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
165 yolo4_5l code 90.38 % 91.79 % 80.64 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
166 AtrousDet 90.35 % 95.94 % 77.94 % 0.05 s TITAN X
167 SCNet
This method makes use of Velodyne laser scans.
90.30 % 95.59 % 85.09 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
168 RUC code 90.24 % 92.60 % 86.55 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
169 Deep3DBox 90.19 % 94.71 % 76.82 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
170 FQNet 90.17 % 94.72 % 76.78 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
171 BVVF 90.15 % 95.65 % 84.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
172 FCY
This method makes use of Velodyne laser scans.
90.15 % 93.27 % 86.60 % 0.02 s GPU @ 2.5 Ghz (Python)
173 SAANet 90.14 % 95.93 % 82.95 % 0.10 s 1 core @ 2.5 Ghz (Python)
174 DeepStereoOP 90.06 % 95.15 % 79.91 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
175 SubCNN 89.98 % 94.26 % 79.78 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
176 MLOD
This method makes use of Velodyne laser scans.
code 89.97 % 94.88 % 84.98 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
177 GPP code 89.96 % 94.02 % 81.13 % 0.23 s GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. arXiv preprint arXiv:1811.06666 2018.
178 RUC code 89.93 % 93.12 % 85.44 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
179 AVOD
This method makes use of Velodyne laser scans.
code 89.88 % 95.17 % 82.83 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
180 SINet_PVA code 89.86 % 92.72 % 76.47 % 0.11 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
181 MCA 89.72 % 93.42 % 79.96 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
182 3DOP
This method uses stereo information.
code 89.55 % 92.96 % 79.38 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
183 IAFA 89.46 % 93.08 % 79.83 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
184 Mono3D code 89.37 % 94.52 % 79.15 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
185 4D-MSCNN+CRL
This method uses stereo information.
89.37 % 92.40 % 77.00 % 0.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
186 R-FCN(FPN) 89.35 % 93.53 % 79.35 % 0.2 s 1 core @ 2.5 Ghz (Python)
187 cas+res+soft 89.14 % 94.54 % 78.37 % 0.2 s 4 cores @ 2.5 Ghz (Python)
188 merge12-12 88.96 % 94.58 % 78.22 % 0.2 s 4 cores @ 2.5 Ghz (Python)
189 Scan_YOLO 88.95 % 90.69 % 79.85 % 0.1 s 4 cores @ 3.0 Ghz (Python)
190 AVOD-FPN
This method makes use of Velodyne laser scans.
code 88.92 % 94.70 % 84.13 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
191 autoRUC 88.88 % 94.23 % 81.35 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
192 Prune 88.85 % 94.20 % 81.31 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
193 AM3D 88.71 % 92.55 % 77.78 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
194 SS3D_HW 88.68 % 94.49 % 68.79 % 0.4 s GPU @ 2.5 Ghz (Python)
195 MS-CNN code 88.68 % 93.87 % 76.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
196 Multi-task DG 88.65 % 93.83 % 76.16 % 0.06 s GPU @ 2.5 Ghz (Python)
197 CRCNNA 88.59 % 94.82 % 76.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
198 3DNN 88.56 % 94.52 % 81.51 % 0.09 s GPU @ 2.5 Ghz (Python)
199 CSFADet 88.54 % 93.75 % 78.62 % 0.05 s GPU @ 2.5 Ghz (Python)
200 MonoPSR code 88.50 % 93.63 % 73.36 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
201 Shift R-CNN (mono) code 88.48 % 94.07 % 78.34 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
202 PSMD 88.47 % 93.67 % 75.62 % 0.1 s GPU @ 2.5 Ghz (Python)
203 RCD 88.46 % 92.52 % 83.73 % 0.1 s GPU @ 2.5 Ghz (Python)
204 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
88.46 % 95.54 % 78.14 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
205 PointRes
This method makes use of Velodyne laser scans.
This method makes use of GPS/IMU information.
This is an online method (no batch processing).
88.41 % 95.38 % 84.22 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
206 AACL 88.35 % 93.56 % 73.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
207 PP-3D 88.35 % 93.71 % 80.84 % 0.1 s 1 core @ 2.5 Ghz (Python)
208 3DBN
This method makes use of Velodyne laser scans.
88.29 % 93.74 % 80.74 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
209 UDI-mono3D 88.16 % 93.93 % 79.57 % 0.05 s 1 core @ 2.5 Ghz (Python)
210 anonymous 88.16 % 96.22 % 75.72 % 1 s 1 core @ 2.5 Ghz (C/C++)
211 ga50 87.65 % 95.76 % 75.14 % 1 s 1 core @ 2.5 Ghz (Python)
212 cas_retina 87.64 % 93.87 % 75.30 % 0.2 s 4 cores @ 2.5 Ghz (Python)
213 SMOKE code 87.51 % 93.21 % 77.66 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
214 MonoSS 87.46 % 93.15 % 77.58 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
215 cascadercnn 87.36 % 89.37 % 73.42 % 0.36 s 4 cores @ 2.5 Ghz (Python)
216 MA 87.29 % 93.21 % 79.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
217 CDN
This method uses stereo information.
87.19 % 95.85 % 79.43 % 0.6 s GPU @ 2.5 Ghz (Python)
218 IMA 87.17 % 92.67 % 77.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
219 RTM3D code 86.93 % 91.82 % 77.41 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
220 yolo_rgb 86.90 % 90.01 % 77.52 % 0.07 s GPU @ 2.5 Ghz (Python)
221 NL_M3D 86.80 % 91.31 % 72.37 % 0.2 s 1 core @ 2.5 Ghz (Python)
222 voxelrcnn 86.69 % 94.60 % 79.91 % 15 s 1 core @ 2.5 Ghz (C/C++)
223 anm 86.52 % 94.88 % 76.46 % 3 s 1 core @ 2.5 Ghz (C/C++)
224 DSGN
This method uses stereo information.
code 86.43 % 95.53 % 78.75 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
225 PB3D
This method uses stereo information.
86.21 % 95.64 % 76.83 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
226 ReSqueeze 86.12 % 90.35 % 76.53 % 0.03 s GPU @ >3.5 Ghz (Python)
227 Stereo R-CNN
This method uses stereo information.
code 85.98 % 93.98 % 71.25 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
228 tiny-stereo-volume 85.79 % 94.62 % 77.91 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
229 StereoFENet
This method uses stereo information.
85.70 % 91.48 % 77.62 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
230 ResNet-RRC w/RGBD 85.58 % 91.32 % 74.80 % 0.057 s GPU @ 1.5 Ghz (Python + C/C++)
231 cas_retina_1_13 85.48 % 91.54 % 74.60 % 0.03 s 4 cores @ 2.5 Ghz (Python)
232 ResNet-RRC_Car 85.33 % 91.45 % 74.27 % 0.06 s GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and . others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
233 Cmerge 85.32 % 93.40 % 70.57 % 0.2 s 4 cores @ 2.5 Ghz (Python)
234 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 85.15 % 94.95 % 77.78 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
235 RAR-Net 85.08 % 89.04 % 69.26 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
236 M3D-RPN code 85.08 % 89.04 % 69.26 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
237 Center3D 85.05 % 95.14 % 73.06 % 0.05 s GPU @ 3.5 Ghz (Python)
238 CDN-PL++
This method uses stereo information.
85.01 % 94.66 % 77.60 % 0.4 s GPU @ 2.5 Ghz (C/C++)
239 SDP+CRC (ft) 85.00 % 92.06 % 71.71 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
240 bifpn_fsrn 84.93 % 93.68 % 74.45 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
241 ResNet-RRC (pruned) 84.93 % 89.59 % 73.26 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
242 IDA-3D
This method uses stereo information.
84.92 % 92.79 % 74.75 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
243 SS3D 84.92 % 92.72 % 70.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
244 ResNet-RRC 84.81 % 89.43 % 73.18 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
245 LPN 84.77 % 89.19 % 74.08 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
246 MonoFENet 84.63 % 91.68 % 76.71 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
247 SECA 84.60 % 92.51 % 79.53 % 1 s GPU @ 2.5 Ghz (Python)
248 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
84.39 % 93.08 % 79.27 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
249 Complexer-YOLO
This method makes use of Velodyne laser scans.
84.16 % 91.92 % 79.62 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
250 HG-Mono 84.01 % 89.65 % 65.28 % 0.46 s GPU @ 2.5 Ghz (C/C++)
251 ZoomNet
This method uses stereo information.
code 83.92 % 94.22 % 69.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
252 D4LCN code 83.67 % 90.34 % 65.33 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
253 seivl 83.60 % 90.35 % 81.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
254 ASOD 83.52 % 94.09 % 68.68 % 0.28 s GPU @ 2.5 Ghz (Python)
255 softretina 83.30 % 93.55 % 70.59 % 0.16 s 4 cores @ 2.5 Ghz (Python)
256 Faster R-CNN code 83.16 % 88.97 % 72.62 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
257 MTMono3d 83.11 % 90.55 % 75.48 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
258 SSL-RTM3D Res18 82.97 % 93.35 % 73.11 % 0.02 s GPU @ 2.5 Ghz (Python)
259 ZKNet 82.96 % 92.17 % 72.43 % 0.01 s GPU @ 2.0 Ghz (Python)
260 Pseudo-LiDAR++
This method uses stereo information.
code 82.90 % 94.46 % 75.45 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
261 DP3D 82.81 % 87.85 % 66.80 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
262 Retinanet100 82.73 % 93.97 % 68.37 % 0.2 s 4 cores @ 2.5 Ghz (Python)
263 BS3D 82.72 % 95.35 % 70.01 % 22 ms Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
264 DP3D 82.63 % 87.90 % 66.62 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
265 Disp R-CNN
This method uses stereo information.
code 82.57 % 93.26 % 68.20 % 0.42 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.
266 Pseudo-LiDAR E2E
This method uses stereo information.
82.54 % 94.00 % 75.31 % 0.4 s GPU @ 2.5 Ghz (Python)
267 Disp R-CNN (velo)
This method uses stereo information.
code 82.47 % 93.20 % 68.09 % 0.42 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.
268 cascade_gw 82.35 % 85.98 % 71.60 % 0.2 s 4 cores @ 2.5 Ghz (Python)
269 deprecated 82.23 % 92.21 % 67.87 % 1 core @ 2.5 Ghz (C/C++)
270 S3D 82.18 % 91.77 % 67.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
271 Stereo3D
This method uses stereo information.
82.15 % 94.81 % 62.17 % 0.1 s GPU 1080Ti
272 LNET 82.02 % 91.49 % 67.71 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
273 FRCNN+Or code 82.00 % 92.91 % 68.79 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
274 CBNet 81.70 % 91.47 % 72.02 % 1 s 4 cores @ 2.5 Ghz (Python)
275 Resnet101Faster rcnn 81.44 % 91.08 % 71.52 % 1 s 1 core @ 2.5 Ghz (Python)
276 yyyyolo 81.33 % 94.36 % 68.72 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
277 LCD3D 81.25 % 91.29 % 64.55 % 0.03 s GPU @ 2.5 Ghz (Python)
278 A3DODWTDA (image) code 81.25 % 78.96 % 70.56 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
279 RefineNet 81.01 % 91.91 % 65.67 % 0.20 s GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016.
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
280 MTDP 80.97 % 89.03 % 66.91 % 0.15 s GPU @ 2.0 Ghz (Python)
281 RFCN_RFB 80.89 % 88.07 % 69.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
282 centernet 80.78 % 90.29 % 70.53 % 0.01 s GPU @ 2.5 Ghz (Python)
283 UM3D_TUM 80.36 % 92.88 % 65.95 % 0.05 s 1 core @ 2.5 Ghz (Python)
284 3D-GCK 80.19 % 89.55 % 68.08 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
285 RADNet-Fusion
This method makes use of Velodyne laser scans.
80.04 % 76.72 % 76.78 % 0.1 s 1 core @ 2.5 Ghz (Python)
286 YoloMono3D 79.63 % 92.37 % 59.69 % 0.05 s GPU @ 2.5 Ghz (Python)
287 RADNet-LIDAR
This method makes use of Velodyne laser scans.
79.59 % 75.20 % 76.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
288 MMRetina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
79.53 % 89.66 % 69.52 % 0.38 s GPU @ 2.5 Ghz (Python)
289 DA-3Ddet 79.47 % 89.49 % 63.04 % 0.4 s GPU @ 2.5 Ghz (Python)
290 SceneNet 79.26 % 90.70 % 67.98 % 0.03 s GPU @ 2.5 Ghz (C/C++)
291 A3DODWTDA
This method makes use of Velodyne laser scans.
code 79.15 % 82.98 % 68.30 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
292 MTNAS 78.82 % 88.96 % 67.07 % 0.02 s 1 core @ 2.5 Ghz (python)
293 spLBP 78.66 % 81.66 % 61.69 % 1.5 s 8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
294 ITS-MDPL 78.41 % 92.43 % 71.30 % 0.16 s GPU @ 2.5 Ghz (Python)
295 3D-SSMFCNN code 78.19 % 77.92 % 69.19 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
296 MonoGRNet code 77.94 % 88.65 % 63.31 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
297 yolov3_warp 77.61 % 92.24 % 65.70 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
298 Reinspect code 77.48 % 90.27 % 66.73 % 2s 1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
299 multi-task CNN 77.18 % 86.12 % 68.09 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
300 Regionlets 76.99 % 88.75 % 60.49 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
301 3DVP code 76.98 % 84.95 % 65.78 % 40 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
302 avodC 76.58 % 87.30 % 71.65 % 0.1 s GPU @ 2.5 Ghz (Python)
303 SubCat code 76.36 % 84.10 % 60.56 % 0.7 s 6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
304 GS3D 76.35 % 86.23 % 62.67 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
305 AOG code 76.24 % 86.08 % 61.51 % 3 s 4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
306 bin 76.16 % 78.73 % 63.39 % 15ms s GPU @ >3.5 Ghz (Python)
307 Pose-RCNN 75.83 % 89.59 % 64.06 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
308 VoxelNet(Unofficial) 75.22 % 81.37 % 68.74 % 0.5 s GPU @ 2.0 Ghz (Python)
309 RFCN 75.14 % 83.04 % 61.55 % 0.2 s 4 cores @ 2.5 Ghz (Python)
310 3D FCN
This method makes use of Velodyne laser scans.
74.65 % 86.74 % 67.85 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
311 OC Stereo
This method uses stereo information.
code 74.60 % 87.39 % 62.56 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
312 yolo_depth 74.40 % 88.71 % 65.58 % 0.07 s GPU @ 2.5 Ghz (Python)
313 yolo800 74.31 % 78.93 % 63.83 % 0.13 s 4 cores @ 2.5 Ghz (Python)
314 3DVSSD 74.11 % 86.99 % 63.57 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
315 FD2 73.93 % 88.65 % 64.62 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
316 BdCost+DA+BB+MS 73.72 % 85.18 % 57.79 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
317 m-prcnn
This method uses stereo information.
73.64 % 87.64 % 57.03 % 0.43 s 1 core @ 2.5 Ghz (Python)
318 BdCost+DA+MS 73.62 % 85.03 % 58.94 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
319 Int-YOLO code 73.23 % 75.81 % 63.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
320 stereo_sa
This method uses stereo information.
72.99 % 87.88 % 63.49 % 0.3 s GPU @ 2.5 Ghz (Python)
321 RuiRUC 72.08 % 87.48 % 55.28 % 0.12 s 1 core @ 2.5 Ghz (Python)
322 ANM 71.97 % 87.17 % 55.19 % 0.12 s 1 core @ 2.5 Ghz (Python)
323 Kinematic3D code 71.73 % 89.67 % 54.97 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
324 RFBnet 71.66 % 87.25 % 63.00 % 0.2 s 4 cores @ 2.5 Ghz (Python)
325 AOG-View 71.26 % 85.01 % 55.73 % 3 s 1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
326 GPVL 71.06 % 81.67 % 54.96 % 10 s 1 core @ 2.5 Ghz (C/C++)
327 BdCost+DA+BB 70.86 % 85.52 % 56.19 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
328 DAM 70.78 % 90.08 % 61.38 % 1 s GPU @ 2.5 Ghz (Python)
329 MV-RGBD-RF
This method makes use of Velodyne laser scans.
70.70 % 77.89 % 57.41 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
330 Vote3Deep
This method makes use of Velodyne laser scans.
70.30 % 78.95 % 63.12 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
331 ROI-10D 70.16 % 76.56 % 61.15 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
332 fasterrcnn 69.45 % 74.76 % 60.20 % 0.2 s 4 cores @ 2.5 Ghz (Python)
333 Decoupled-3D v2 68.17 % 88.64 % 54.74 % 0.08 s GPU @ 2.5 Ghz (C/C++)
334 BirdNet+
This method makes use of Velodyne laser scans.
code 68.05 % 92.10 % 65.61 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
335 Decoupled-3D 67.92 % 87.78 % 54.53 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
336 Pseudo-Lidar
This method uses stereo information.
code 67.79 % 85.40 % 58.50 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
337 OC-DPM 67.06 % 79.07 % 52.61 % 10 s 8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
338 Fast-SSD 66.79 % 85.19 % 57.89 % 0.06 s GTX650Ti
339 DPM-VOC+VP 66.72 % 82.15 % 49.01 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
340 BdCost48LDCF code 66.63 % 81.38 % 52.20 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
341 RefinedMPL 65.24 % 88.29 % 53.20 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
342 BdCost48-25C 64.63 % 81.42 % 52.22 % 4 s 1 core @ 2.5 Ghz (C/C++)
343 MDPM-un-BB 64.06 % 79.74 % 49.07 % 60 s 4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
344 TLNet (Stereo)
This method uses stereo information.
code 63.53 % 76.92 % 54.58 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
345 PDV-Subcat 63.24 % 78.27 % 47.67 % 7 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
346 PG-MonoNet 62.75 % 70.87 % 54.34 % 0.19 s GPU @ 2.5 Ghz (Python)
347 MODet
This method makes use of Velodyne laser scans.
62.54 % 66.06 % 60.04 % 0.05 s GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
348 yl_net 61.78 % 66.00 % 60.36 % 0.03 s GPU @ 2.5 Ghz (Python)
349 Lidar_ROI+Yolo(UJS) 61.71 % 73.32 % 53.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
350 GNN 61.48 % 79.09 % 51.06 % 0.2 s 1 core @ 2.5 Ghz (Python)
351 SubCat48LDCF code 61.16 % 78.86 % 44.69 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
352 DPM-C8B1
This method uses stereo information.
60.21 % 75.24 % 44.73 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
353 SDP-Net-s 59.94 % 65.51 % 57.20 % 12ms GPU @ 2.5 Ghz (Python)
354 RADNet-Mono 59.85 % 67.47 % 54.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
355 monoref3d 58.97 % 78.11 % 47.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
356 ref3D 58.97 % 78.11 % 47.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
357 100Frcnn 58.92 % 82.09 % 49.04 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
358 SAMME48LDCF code 58.38 % 77.47 % 44.43 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
359 LSVM-MDPM-sv 58.36 % 71.11 % 43.22 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
360 ref3D 57.16 % 77.96 % 45.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
361 BirdNet
This method makes use of Velodyne laser scans.
57.12 % 79.30 % 55.16 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
362 ACF-SC 56.60 % 69.90 % 43.61 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
363 LSVM-MDPM-us code 55.95 % 68.94 % 41.45 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
364 ACF 54.09 % 63.05 % 41.81 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
365 Mono3D_PLiDAR code 53.36 % 80.85 % 44.80 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
366 RT3D-GMP
This method uses stereo information.
51.95 % 62.41 % 39.14 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
367 VeloFCN
This method makes use of Velodyne laser scans.
51.82 % 70.53 % 45.70 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
368 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
46.68 % 60.62 % 38.22 % 0.5 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
369 softyolo 45.97 % 66.08 % 38.02 % 0.16 s 4 cores @ 2.5 Ghz (Python)
370 Vote3D
This method makes use of Velodyne laser scans.
45.94 % 54.38 % 40.48 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
371 TopNet-HighRes
This method makes use of Velodyne laser scans.
45.85 % 58.04 % 41.11 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
372 RT3DStereo
This method uses stereo information.
45.81 % 56.53 % 37.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
373 Multimodal Detection
This method makes use of Velodyne laser scans.
code 45.46 % 63.91 % 37.25 % 0.06 s GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
374 RT3D
This method makes use of Velodyne laser scans.
39.69 % 50.33 % 40.04 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
375 VoxelJones code 36.31 % 43.89 % 34.16 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
376 Licar
This method makes use of Velodyne laser scans.
35.19 % 42.34 % 33.97 % 0.09 s GPU @ 2.0 Ghz (Python)
377 KD53-20 34.76 % 51.76 % 29.39 % 0.19 s 4 cores @ 2.5 Ghz (Python)
378 SAIC-SA-3D
This method makes use of Velodyne laser scans.
31.16 % 41.51 % 29.83 % 0.05 s GPU @ 2.5 Ghz (Python)
379 FCN-Depth code 25.05 % 52.32 % 18.07 % 1 s GPU @ 1.5 Ghz (Matlab + C/C++)
380 CSoR
This method makes use of Velodyne laser scans.
code 21.66 % 31.52 % 17.99 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
381 mBoW
This method makes use of Velodyne laser scans.
21.59 % 35.22 % 16.89 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
382 R-CNN_VGG 21.36 % 29.38 % 16.61 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
383 DepthCN
This method makes use of Velodyne laser scans.
code 21.18 % 37.45 % 16.08 % 2.3 s GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
384 YOLOv2 code 14.31 % 26.74 % 10.94 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
385 TopNet-UncEst
This method makes use of Velodyne laser scans.
6.24 % 7.24 % 5.42 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
386 TopNet-Retina
This method makes use of Velodyne laser scans.
5.00 % 6.82 % 4.52 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
387 ANM 0.01 % 0.01 % 0.02 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
388 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.01 % 0.00 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
389 LaserNet 0.00 % 0.00 % 0.00 % 12 ms GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
390 JSyolo 0.00 % 0.00 % 0.00 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 HWFD 83.06 % 90.50 % 78.35 % 0.21 s one 1080Ti
2 FichaDL 82.50 % 90.75 % 75.66 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
3 DGIST-CellBox 81.29 % 90.04 % 76.92 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
4 Alibaba-CityBrain 81.19 % 90.99 % 74.68 % 1.5 s GPU @ 2.5 Ghz (Python + C/C++)
5 ExtAtt 81.05 % 90.60 % 76.08 % 1.2 s GPU @ 2.5 Ghz (Python + C/C++)
6 dgist_multiDetNet 80.21 % 89.21 % 75.77 % 0.08 s GPU Titanx Pascal (Python)
7 F-PointNet
This method makes use of Velodyne laser scans.
code 80.13 % 89.83 % 75.05 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
8 TuSimple code 78.40 % 88.87 % 73.66 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
9 Argus_detection_v1 77.01 % 84.86 % 72.15 % 0.25 s GPU @ 1.5 Ghz (C/C++)
10 RRC code 76.61 % 85.98 % 71.47 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
11 ECP Faster R-CNN 76.25 % 85.96 % 70.55 % 0.25 s GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
12 Aston-EAS 76.07 % 86.71 % 70.02 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
13 MHN 75.99 % 87.21 % 69.50 % 0.39 s GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.
14 FFNet code 75.81 % 87.17 % 69.86 % 1.07 s GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
15 SJTU-HW 75.81 % 87.17 % 69.86 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
16 Noah CV Lab - SSL 75.64 % 86.57 % 70.53 % 0.1 s GPU @ 2.5 Ghz (Python)
17 Faster RCNN + Gr + A 74.95 % 86.95 % 69.50 % 1.29 s GPU @ 2.5 Ghz (Python)
18 MS-CNN code 74.89 % 85.71 % 68.99 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
19 Multi-task DG 73.99 % 85.21 % 68.06 % 0.06 s GPU @ 2.5 Ghz (Python)
20 Faster RCNN + G 73.75 % 85.51 % 68.54 % 1.1 s GPU @ 2.5 Ghz (Python)
21 F-ConvNet
This method makes use of Velodyne laser scans.
code 72.91 % 83.63 % 67.18 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
22 Sogo_MM 72.82 % 84.99 % 67.42 % 1.5 s GPU @ 2.5 Ghz (C/C++)
23 Faster RCNN + A 72.67 % 86.21 % 67.55 % 0.19 s GPU @ 2.5 Ghz (Python)
24 GN 72.29 % 82.93 % 65.56 % 1 s GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
25 SubCNN 72.27 % 84.88 % 66.82 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
26 FRCNN-WS 72.26 % 84.20 % 67.47 % 0.22 s 1 core @ 3.0 Ghz (Python)
27 HR-faster-rcnn 72.26 % 87.65 % 65.71 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
28 Faster RCNN + A 72.09 % 85.35 % 66.87 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
29 VMVS
This method makes use of Velodyne laser scans.
71.82 % 82.80 % 66.85 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
30 IVA code 71.37 % 84.61 % 64.90 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
31 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
70.76 % 83.79 % 64.81 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
32 SDP+RPN 70.42 % 82.07 % 65.09 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
33 CSFADet 70.07 % 84.72 % 64.81 % 0.05 s GPU @ 2.5 Ghz (Python)
34 Mono3CN 69.75 % 83.47 % 63.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
35 3DOP
This method uses stereo information.
code 69.57 % 83.17 % 63.48 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
36 MonoPSR code 68.56 % 85.60 % 63.34 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
37 DeepStereoOP 68.46 % 83.00 % 63.35 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
38 sensekitti code 68.41 % 82.72 % 62.72 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
39 FII-CenterNet 67.31 % 81.32 % 61.29 % 0.09 s GPU @ 2.5 Ghz (Python)
40 Mono3D code 67.29 % 80.30 % 62.23 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
41 Faster R-CNN code 66.24 % 79.97 % 61.09 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
42 AtrousDet 64.97 % 80.79 % 58.36 % 0.05 s TITAN X
43 SDP+CRC (ft) 64.36 % 79.22 % 59.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
44 CRCNNA 63.69 % 78.10 % 58.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 Pose-RCNN 63.54 % 80.07 % 57.02 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
46 PCN 63.41 % 80.08 % 58.55 % 0.6 s
47 CFM 62.84 % 74.76 % 56.06 % <2 s GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
48 merge12-12 62.84 % 80.27 % 56.08 % 0.2 s 4 cores @ 2.5 Ghz (Python)
49 cas+res+soft 62.71 % 80.11 % 55.99 % 0.2 s 4 cores @ 2.5 Ghz (Python)
50 cas_retina 62.37 % 79.82 % 57.15 % 0.2 s 4 cores @ 2.5 Ghz (Python)
51 HotSpotNet 62.31 % 71.43 % 59.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
52 UDI-mono3D 62.26 % 77.16 % 56.39 % 0.05 s 1 core @ 2.5 Ghz (Python)
53 cas_retina_1_13 61.87 % 79.09 % 56.70 % 0.03 s 4 cores @ 2.5 Ghz (Python)
54 MonoPair 61.57 % 78.81 % 56.51 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
55 ReSqueeze 61.33 % 73.69 % 56.65 % 0.03 s GPU @ >3.5 Ghz (Python)
56 RPN+BF code 61.22 % 77.06 % 55.22 % 0.6 s GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
57 JSU-NET 61.19 % 83.17 % 56.20 % 0.1 s 1 core @ 2.5 Ghz (Python)
58 RethinkDet3D 60.88 % 70.56 % 56.69 % 0.15 s 1 core @ 2.5 Ghz (Python)
59 Regionlets 60.83 % 73.79 % 54.72 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
60 bin 60.73 % 71.43 % 55.78 % 15ms s GPU @ >3.5 Ghz (Python)
61 60.63 % 69.37 % 57.64 %
62 3DSSD code 60.51 % 72.33 % 56.28 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
63 anm 60.35 % 76.02 % 55.46 % 3 s 1 core @ 2.5 Ghz (C/C++)
64 MVX-Net++ 60.21 % 69.70 % 56.07 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
65 PiP 59.94 % 70.52 % 56.51 % 0.033 s 1 core @ 2.5 Ghz (Python)
66 cascadercnn 59.50 % 78.79 % 54.44 % 0.36 s 4 cores @ 2.5 Ghz (Python)
67 TANet code 59.07 % 69.90 % 56.44 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
68 58.70 % 68.18 % 54.68 %
69 FDet 58.68 % 68.44 % 56.22 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
70 DDB
This method makes use of Velodyne laser scans.
58.53 % 69.03 % 55.90 % 0.05 s GPU @ 2.5 Ghz (Python)
71 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 58.37 % 68.88 % 55.38 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
72 Point-GNN
This method makes use of Velodyne laser scans.
code 58.20 % 71.59 % 54.06 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
73 DeepParts 58.15 % 71.47 % 51.92 % ~1 s GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
74 CompACT-Deep 58.14 % 70.93 % 52.29 % 1 s 1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
75 PPBA 58.06 % 67.73 % 55.69 % NA s GPU @ 2.5 Ghz (Python)
76 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 57.96 % 68.78 % 54.01 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
77 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.87 % 67.95 % 55.23 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
78 LPN 57.69 % 71.87 % 53.21 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
79 TBU 57.44 % 67.29 % 54.00 % NA s GPU @ 2.5 Ghz (Python)
80 CentrNet-FG 57.40 % 68.27 % 54.11 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
81 Simple3D Net 57.00 % 66.89 % 54.38 % 0.02 s GPU @ 2.5 Ghz (Python)
82 KNN-GCNN 56.80 % 69.53 % 52.86 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
83 LDAM 56.68 % 64.73 % 54.21 % 24 ms GTX 1080 ti GPU
84 FRCNN+Or code 56.68 % 71.64 % 51.53 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
85 yolo800 56.67 % 71.26 % 50.91 % 0.13 s 4 cores @ 2.5 Ghz (Python)
86 ZKNet 56.58 % 71.15 % 51.87 % 0.01 s GPU @ 2.0 Ghz (Python)
87 CentrNet-v1
This method makes use of Velodyne laser scans.
56.57 % 66.27 % 54.19 % 0.03 s GPU @ 2.5 Ghz (Python)
88 FilteredICF 56.53 % 69.79 % 50.32 % ~ 2 s >8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
89 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
56.49 % 66.91 % 54.06 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
90 ARPNET 56.42 % 69.08 % 52.69 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
91 FD2 56.35 % 71.37 % 51.08 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
92 MV-RGBD-RF
This method makes use of Velodyne laser scans.
56.18 % 72.99 % 49.72 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
93 RFCN 55.96 % 72.32 % 49.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
94 yolo4 55.78 % 72.49 % 51.11 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
95 MLOD
This method makes use of Velodyne laser scans.
code 55.62 % 68.42 % 51.45 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
96 DAM 55.60 % 74.85 % 50.63 % 1 s GPU @ 2.5 Ghz (Python)
97 yolo4_5l 55.29 % 74.54 % 48.20 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
98 PointPillars
This method makes use of Velodyne laser scans.
code 55.10 % 65.29 % 52.39 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
99 STD code 55.04 % 68.33 % 50.85 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
100 RFCN_RFB 54.98 % 70.61 % 48.75 % 0.2 s 4 cores @ 2.5 Ghz (Python)
101 Vote3Deep
This method makes use of Velodyne laser scans.
54.80 % 67.99 % 51.17 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
102 yolo4 54.30 % 73.16 % 49.46 % 0.02 s 1 core @ 2.5 Ghz (Python)
103 CHTTL MMF 54.28 % 72.79 % 49.31 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
104 epBRM
This method makes use of Velodyne laser scans.
code 54.13 % 62.90 % 51.95 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
105 PointPainting
This method makes use of Velodyne laser scans.
53.76 % 61.86 % 50.61 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
106 PDV2 53.54 % 65.59 % 47.65 % 3.7 s 1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
107 fasterrcnn 53.42 % 69.29 % 48.76 % 0.2 s 4 cores @ 2.5 Ghz (Python)
108 3DBN_2 53.26 % 63.82 % 50.76 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
109 TAFT 53.15 % 67.62 % 47.08 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
110 MTMono3d 52.96 % 69.01 % 46.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
111 pAUCEnsT 52.88 % 65.84 % 46.97 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
112 yolo4_5l code 52.74 % 71.89 % 47.90 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
113 deprecated 52.32 % 67.93 % 47.77 % 0.05 s GPU @ 2.0 Ghz (Python)
114 PP-3D 52.11 % 63.07 % 49.79 % 0.1 s 1 core @ 2.5 Ghz (Python)
115 ResNet-RRC 52.09 % 66.44 % 47.51 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
116 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
51.83 % 67.73 % 47.45 % 0.5 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
117 MTDP 51.81 % 68.12 % 46.95 % 0.15 s GPU @ 2.0 Ghz (Python)
118 Disp R-CNN
This method uses stereo information.
code 51.36 % 68.93 % 46.79 % 0.42 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.
119 TBD 51.31 % 61.14 % 47.82 % 0.05 s GPU @ 2.5 Ghz (Python)
120 Disp R-CNN (velo)
This method uses stereo information.
code 51.31 % 68.84 % 46.80 % 0.42 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.
121 Shift R-CNN (mono) code 51.30 % 70.86 % 46.37 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
122 ResNet-RRC (pruned) 51.12 % 65.47 % 46.53 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
123 centernet 51.09 % 69.27 % 45.40 % 0.01 s GPU @ 2.5 Ghz (Python)
124 FCY
This method makes use of Velodyne laser scans.
50.88 % 59.73 % 48.61 % 0.02 s GPU @ 2.5 Ghz (Python)
125 PPFNet code 50.52 % 57.82 % 47.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
126 PVNet 50.50 % 60.58 % 48.48 % 0,1 s 1 core @ 2.5 Ghz (Python)
127 SCNet
This method makes use of Velodyne laser scans.
49.61 % 60.95 % 46.91 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
128 yolo_depth 49.47 % 67.23 % 44.99 % 0.07 s GPU @ 2.5 Ghz (Python)
129 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 49.41 % 58.93 % 46.44 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
130 Resnet101Faster rcnn 49.12 % 64.72 % 44.60 % 1 s 1 core @ 2.5 Ghz (Python)
131 VOXEL_FPN_HR 49.09 % 60.28 % 45.47 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
132 SS3D_HW 49.01 % 64.67 % 42.86 % 0.4 s GPU @ 2.5 Ghz (Python)
133 cascade_gw 48.99 % 67.35 % 44.25 % 0.2 s 4 cores @ 2.5 Ghz (Python)
134 Int-YOLO code 48.76 % 64.09 % 44.31 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
135 Center3D 48.76 % 67.15 % 44.05 % 0.05 s GPU @ 3.5 Ghz (Python)
136 MP 48.73 % 60.26 % 45.05 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
137 ACFD
This method makes use of Velodyne laser scans.
code 48.63 % 61.62 % 44.15 % 0.2 s 4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
138 R-CNN 48.57 % 62.88 % 43.05 % 4 s GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
139 yolo_rgb 48.45 % 64.50 % 43.95 % 0.07 s GPU @ 2.5 Ghz (Python)
140 PBASN code 46.75 % 54.38 % 44.58 % NA s GPU @ 2.5 Ghz (Python)
141 HR-SECOND code 46.69 % 58.68 % 42.93 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
142 Cmerge 46.51 % 63.68 % 41.60 % 0.2 s 4 cores @ 2.5 Ghz (Python)
143 NLK-3D 46.33 % 59.46 % 43.88 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
144 SS3D 45.79 % 61.58 % 41.14 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
145 ACF 45.67 % 59.81 % 40.88 % 1 s 1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
146 NL_M3D 45.03 % 58.46 % 39.22 % 0.2 s 1 core @ 2.5 Ghz (Python)
147 Fusion-DPM
This method makes use of Velodyne laser scans.
code 44.99 % 58.93 % 40.19 % ~ 30 s 1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
148 ACF-MR 44.79 % 58.29 % 39.94 % 0.6 s 1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
149 yyyyolo 44.55 % 60.74 % 39.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
150 HA-SSVM 43.87 % 58.76 % 38.81 % 21 s 1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
151 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 43.86 % 54.55 % 40.99 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
152 D4LCN code 43.50 % 59.55 % 37.12 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
153 Pointpillar_TV 43.29 % 53.06 % 41.14 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
154 DPM-VOC+VP 43.26 % 59.21 % 38.12 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
155 ACF-SC 42.97 % 53.30 % 38.12 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
156 SquaresICF code 42.61 % 57.08 % 37.85 % 1 s GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
157 CG-Stereo
This method uses stereo information.
42.54 % 54.64 % 38.45 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
158 DP3D 42.33 % 57.82 % 38.11 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
159 GNN 42.28 % 58.09 % 37.81 % 0.2 s 1 core @ 2.5 Ghz (Python)
160 BirdNet+
This method makes use of Velodyne laser scans.
code 41.97 % 51.38 % 40.15 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
161 DP3D 41.71 % 55.28 % 35.73 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
162 MMRetina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
41.63 % 59.63 % 36.97 % 0.38 s GPU @ 2.5 Ghz (Python)
163 CSW3D
This method makes use of Velodyne laser scans.
41.50 % 53.76 % 37.25 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
164 HG-Mono 41.48 % 56.67 % 37.33 % 0.46 s GPU @ 2.5 Ghz (C/C++)
165 M3D-RPN code 41.46 % 56.64 % 37.31 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
166 Stereo3D
This method uses stereo information.
41.46 % 56.20 % 37.07 % 0.1 s GPU 1080Ti
167 yolov3_warp 40.64 % 55.04 % 36.33 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
168 SubCat 40.50 % 53.75 % 35.66 % 1.2 s 6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
169 SAANet 40.43 % 51.16 % 38.38 % 0.10 s 1 core @ 2.5 Ghz (Python)
170 Retinanet100 40.03 % 54.30 % 35.33 % 0.2 s 4 cores @ 2.5 Ghz (Python)
171 DSGN
This method uses stereo information.
code 39.93 % 49.28 % 38.13 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
172 RT3D-GMP
This method uses stereo information.
39.83 % 55.56 % 35.18 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
173 SparsePool code 39.59 % 50.81 % 35.91 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
174 SparsePool code 39.43 % 50.94 % 35.78 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
175 AVOD
This method makes use of Velodyne laser scans.
code 39.43 % 50.90 % 35.75 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
176 PG-MonoNet 39.38 % 48.57 % 35.43 % 0.19 s GPU @ 2.5 Ghz (Python)
177 softyolo 39.30 % 54.49 % 36.66 % 0.16 s 4 cores @ 2.5 Ghz (Python)
178 ACF 39.12 % 48.42 % 35.03 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
179 PB3D
This method uses stereo information.
38.62 % 50.26 % 34.87 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
180 pedestrian_cnn 37.90 % 52.07 % 33.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
181 LSVM-MDPM-sv 37.26 % 50.74 % 33.13 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
182 multi-task CNN 37.00 % 49.38 % 33.46 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
183 Complexer-YOLO
This method makes use of Velodyne laser scans.
36.45 % 42.16 % 32.91 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
184 KD53-20 36.03 % 45.78 % 32.79 % 0.19 s 4 cores @ 2.5 Ghz (Python)
185 LSVM-MDPM-us code 35.92 % 48.73 % 31.70 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
186 Lidar_ROI+Yolo(UJS) 35.58 % 47.74 % 31.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
187 34.81 % 44.38 % 32.10 %
188 Vote3D
This method makes use of Velodyne laser scans.
33.04 % 42.66 % 30.59 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
189 OC Stereo
This method uses stereo information.
code 30.79 % 43.50 % 28.40 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
190 mBoW
This method makes use of Velodyne laser scans.
30.26 % 41.52 % 26.34 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
191 BirdNet
This method makes use of Velodyne laser scans.
30.07 % 36.82 % 28.40 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
192 RT3DStereo
This method uses stereo information.
29.30 % 41.12 % 25.25 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
193 DPM-C8B1
This method uses stereo information.
25.34 % 36.40 % 22.00 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
194 100Frcnn 21.92 % 34.07 % 19.48 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
195 RefinedMPL 20.81 % 30.41 % 18.72 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
196 R-CNN_VGG 19.97 % 26.62 % 17.96 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
197 TopNet-Retina
This method makes use of Velodyne laser scans.
16.45 % 22.37 % 15.43 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
198 TopNet-HighRes
This method makes use of Velodyne laser scans.
15.28 % 21.22 % 13.89 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
199 YOLOv2 code 11.46 % 15.37 % 9.67 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
200 TopNet-UncEst
This method makes use of Velodyne laser scans.
8.58 % 13.00 % 7.38 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
201 BIP-HETERO 7.05 % 8.51 % 6.30 % ~2 s 1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
202 CBNet 1.33 % 1.03 % 1.41 % 1 s 4 cores @ 2.5 Ghz (Python)
203 softretina 0.26 % 0.19 % 0.26 % 0.16 s 4 cores @ 2.5 Ghz (Python)
204 JSyolo 0.12 % 0.19 % 0.12 % 0.16 s 4 cores @ 2.5 Ghz (Python)
205 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.01 % 0.01 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
206 UM3D_TUM 0.00 % 0.00 % 0.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 HRI-MSP-L
This method makes use of Velodyne laser scans.
83.08 % 92.12 % 75.62 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
2 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 80.42 % 86.62 % 73.64 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
3 FichaDL 80.38 % 88.41 % 69.72 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
4 Noah CV Lab - SSL 79.10 % 86.71 % 69.66 % 0.1 s GPU @ 2.5 Ghz (Python)
5 HotSpotNet 78.81 % 86.06 % 71.74 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
6 78.42 % 85.79 % 71.80 %
7 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.29 % 88.90 % 71.19 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
8 F-ConvNet
This method makes use of Velodyne laser scans.
code 78.05 % 86.75 % 68.12 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
9 PointPainting
This method makes use of Velodyne laser scans.
78.04 % 87.70 % 69.27 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
10 TBD 77.34 % 87.15 % 70.53 % 0.05 s GPU @ 2.5 Ghz (Python)
11 RRC code 76.81 % 86.81 % 66.59 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
12 KNN-GCNN 76.52 % 88.83 % 69.82 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
13 75.98 % 83.71 % 68.80 %
14 HWFD 75.54 % 85.88 % 66.85 % 0.21 s one 1080Ti
15 MVX-Net++ 75.41 % 86.78 % 68.49 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
16 MS-CNN code 75.30 % 84.88 % 65.27 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
17 VOXEL_FPN_HR 75.24 % 87.73 % 68.60 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
18 RethinkDet3D 75.22 % 89.04 % 66.47 % 0.15 s 1 core @ 2.5 Ghz (Python)
19 TuSimple code 75.22 % 83.68 % 65.22 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
20 Point-GNN
This method makes use of Velodyne laser scans.
code 75.08 % 85.75 % 68.69 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
21 ExtAtt 75.08 % 86.09 % 65.30 % 1.2 s GPU @ 2.5 Ghz (Python + C/C++)
22 Deep3DBox 74.78 % 84.36 % 64.05 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
23 PPBA 74.46 % 86.45 % 69.15 % NA s GPU @ 2.5 Ghz (Python)
24 TBU 74.46 % 86.45 % 69.15 % NA s GPU @ 2.5 Ghz (Python)
25 3DBN_2 74.34 % 88.48 % 67.66 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
26 3DSSD code 74.12 % 87.09 % 67.67 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
27 Multi-task DG 74.05 % 82.73 % 64.21 % 0.06 s GPU @ 2.5 Ghz (Python)
28 SDP+RPN 73.85 % 82.59 % 64.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
29 FDet 73.69 % 85.39 % 66.94 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
30 dgist_multiDetNet 73.57 % 87.95 % 64.65 % 0.08 s GPU Titanx Pascal (Python)
31 sensekitti code 73.48 % 82.90 % 64.03 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
32 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 73.42 % 86.21 % 66.45 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
33 F-PointNet
This method makes use of Velodyne laser scans.
code 73.16 % 86.86 % 65.21 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
34 HR-SECOND code 72.77 % 84.21 % 66.25 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
35 MonoPSR code 72.08 % 82.06 % 62.43 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
36 ARPNET 71.95 % 84.96 % 65.21 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
37 SubCNN 71.72 % 79.36 % 62.74 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
38 STD code 71.63 % 83.99 % 64.92 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
39 Sogo_MM 71.57 % 79.35 % 62.22 % 1.5 s GPU @ 2.5 Ghz (C/C++)
40 PiP 71.52 % 82.97 % 65.52 % 0.033 s 1 core @ 2.5 Ghz (Python)
41 PVNet 71.10 % 83.89 % 65.08 % 0,1 s 1 core @ 2.5 Ghz (Python)
42 Faster RCNN + Gr + A 70.78 % 83.99 % 63.36 % 1.29 s GPU @ 2.5 Ghz (Python)
43 NLK-3D 70.55 % 85.92 % 63.76 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
44 PBASN code 70.21 % 83.96 % 65.10 % NA s GPU @ 2.5 Ghz (Python)
45 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 70.18 % 82.86 % 63.55 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
46 MP 69.52 % 85.05 % 63.17 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
47 LDAM 69.31 % 80.20 % 63.85 % 24 ms GTX 1080 ti GPU
48 PointPillars
This method makes use of Velodyne laser scans.
code 68.98 % 83.97 % 62.17 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
49 DGIST-CellBox 68.92 % 83.72 % 61.32 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
50 CentrNet-FG 68.88 % 83.29 % 61.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
51 Vote3Deep
This method makes use of Velodyne laser scans.
68.82 % 78.41 % 62.50 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
52 3DOP
This method uses stereo information.
code 68.71 % 80.52 % 61.07 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
53 Pose-RCNN 68.40 % 81.53 % 59.43 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
54 TANet code 68.20 % 82.24 % 62.13 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
55 Faster RCNN + G 68.09 % 83.51 % 60.60 % 1.1 s GPU @ 2.5 Ghz (Python)
56 Faster RCNN + A 67.84 % 82.06 % 60.52 % 0.19 s GPU @ 2.5 Ghz (Python)
57 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
67.82 % 82.74 % 61.06 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
58 IVA code 67.57 % 78.48 % 58.83 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
59 DeepStereoOP 67.22 % 79.35 % 58.60 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
60 Faster RCNN + A 67.15 % 83.77 % 59.85 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
61 SAANet 66.58 % 83.07 % 59.88 % 0.10 s 1 core @ 2.5 Ghz (Python)
62 FII-CenterNet 66.54 % 79.04 % 57.76 % 0.09 s GPU @ 2.5 Ghz (Python)
63 epBRM
This method makes use of Velodyne laser scans.
code 66.51 % 79.65 % 60.31 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
64 Pointpillar_TV 66.20 % 79.86 % 59.73 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
65 HR-faster-rcnn 65.53 % 83.49 % 58.13 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
66 FCY
This method makes use of Velodyne laser scans.
65.50 % 81.33 % 59.04 % 0.02 s GPU @ 2.5 Ghz (Python)
67 Mono3D code 65.15 % 77.19 % 57.88 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
68 Simple3D Net 64.77 % 79.60 % 58.48 % 0.02 s GPU @ 2.5 Ghz (Python)
69 deprecated 63.34 % 83.91 % 53.78 % 0.05 s GPU @ 2.0 Ghz (Python)
70 Mono3CN 63.29 % 81.46 % 56.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 CentrNet-v1
This method makes use of Velodyne laser scans.
62.99 % 78.90 % 56.46 % 0.03 s GPU @ 2.5 Ghz (Python)
72 Faster R-CNN code 62.86 % 72.40 % 54.97 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
73 AtrousDet 62.50 % 79.02 % 53.87 % 0.05 s TITAN X
74 SCNet
This method makes use of Velodyne laser scans.
62.50 % 78.48 % 56.34 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
75 DDB
This method makes use of Velodyne laser scans.
61.41 % 78.04 % 55.37 % 0.05 s GPU @ 2.5 Ghz (Python)
76 PP-3D 61.29 % 77.75 % 54.59 % 0.1 s 1 core @ 2.5 Ghz (Python)
77 AVOD-FPN
This method makes use of Velodyne laser scans.
code 60.79 % 70.38 % 55.37 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
78 SDP+CRC (ft) 60.72 % 75.63 % 53.00 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
79 Complexer-YOLO
This method makes use of Velodyne laser scans.
59.78 % 66.94 % 55.63 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
80 merge12-12 59.48 % 77.66 % 51.41 % 0.2 s 4 cores @ 2.5 Ghz (Python)
81 UDI-mono3D 59.44 % 77.70 % 51.49 % 0.05 s 1 core @ 2.5 Ghz (Python)
82 cas+res+soft 59.43 % 77.85 % 51.34 % 0.2 s 4 cores @ 2.5 Ghz (Python)
83 Regionlets 58.52 % 71.12 % 50.83 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
84 DAM 58.41 % 76.09 % 49.93 % 1 s GPU @ 2.5 Ghz (Python)
85 cascadercnn 58.08 % 77.24 % 51.13 % 0.36 s 4 cores @ 2.5 Ghz (Python)
86 GA_rpn500 57.82 % 76.06 % 49.00 % 1 s 1 core @ 2.5 Ghz (Python)
87 GA2500 57.82 % 76.06 % 48.99 % 0.2 s 1 core @ 2.5 Ghz (Python)
88 bin 57.62 % 64.36 % 50.70 % 15ms s GPU @ >3.5 Ghz (Python)
89 GA_FULLDATA 57.20 % 75.50 % 50.26 % 1 s 4 cores @ 2.5 Ghz (Python)
90 cas_retina 57.14 % 73.97 % 50.32 % 0.2 s 4 cores @ 2.5 Ghz (Python)
91 FRCNN+Or code 57.01 % 70.99 % 50.14 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
92 CSFADet 56.88 % 73.82 % 50.22 % 0.05 s GPU @ 2.5 Ghz (Python)
93 cas_retina_1_13 56.39 % 72.80 % 49.71 % 0.03 s 4 cores @ 2.5 Ghz (Python)
94 MonoPair 56.37 % 74.77 % 48.37 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
95 GA_BALANCE 56.07 % 78.33 % 49.02 % 1 s 1 core @ 2.5 Ghz (Python)
96 MLOD
This method makes use of Velodyne laser scans.
code 56.04 % 75.35 % 49.11 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
97 bigger_ga 55.66 % 73.05 % 47.31 % 1 s 1 core @ 2.5 Ghz (Python)
98 BirdNet+
This method makes use of Velodyne laser scans.
code 54.61 % 74.97 % 50.29 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
99 ReSqueeze 54.50 % 69.64 % 48.24 % 0.03 s GPU @ >3.5 Ghz (Python)
100 NL_M3D 53.51 % 71.09 % 47.07 % 0.2 s 1 core @ 2.5 Ghz (Python)
101 CRCNNA 53.41 % 69.81 % 46.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
102 AVOD
This method makes use of Velodyne laser scans.
code 52.60 % 66.45 % 46.39 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
103 GAFM 51.40 % 73.43 % 44.61 % 0.5 s 1 core @ 2.5 Ghz (Python)
104 JSU-NET 51.10 % 72.92 % 44.26 % 0.1 s 1 core @ 2.5 Ghz (Python)
105 yolo4_5l 50.70 % 70.51 % 44.44 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
106 yolo4 50.62 % 71.71 % 44.18 % 0.02 s 1 core @ 2.5 Ghz (Python)
107 HG-Mono 49.55 % 67.69 % 40.89 % 0.46 s GPU @ 2.5 Ghz (C/C++)
108 ZKNet 49.48 % 66.29 % 42.81 % 0.01 s GPU @ 2.0 Ghz (Python)
109 anm 49.05 % 66.96 % 43.44 % 3 s 1 core @ 2.5 Ghz (C/C++)
110 ga50 49.02 % 70.25 % 42.52 % 1 s 1 core @ 2.5 Ghz (Python)
111 yolo4 48.67 % 67.33 % 43.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
112 LPN 48.57 % 65.77 % 42.66 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
113 CG-Stereo
This method uses stereo information.
48.46 % 69.98 % 42.41 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
114 yolo4_5l code 48.38 % 69.14 % 42.16 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
115 fasterrcnn 47.87 % 64.39 % 42.03 % 0.2 s 4 cores @ 2.5 Ghz (Python)
116 MTMono3d 47.71 % 67.12 % 38.84 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
117 BirdNet
This method makes use of Velodyne laser scans.
47.64 % 64.91 % 44.59 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
118 yolo800 47.31 % 63.22 % 42.28 % 0.13 s 4 cores @ 2.5 Ghz (Python)
119 RFCN 46.70 % 62.09 % 40.71 % 0.2 s 4 cores @ 2.5 Ghz (Python)
120 SS3D_HW 45.53 % 61.79 % 39.03 % 0.4 s GPU @ 2.5 Ghz (Python)
121 RFCN_RFB 45.28 % 60.06 % 39.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
122 Cmerge 44.87 % 64.38 % 37.80 % 0.2 s 4 cores @ 2.5 Ghz (Python)
123 SparsePool code 44.57 % 60.53 % 40.37 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
124 Scan_YOLO 43.39 % 64.82 % 37.77 % 0.1 s 4 cores @ 3.0 Ghz (Python)
125 ResNet-RRC (pruned) 43.35 % 58.81 % 37.68 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
126 Shift R-CNN (mono) code 42.96 % 63.24 % 38.22 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
127 ResNet-RRC 42.88 % 58.72 % 37.74 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
128 D4LCN code 42.86 % 65.29 % 36.29 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
129 cascade_gw 42.84 % 63.58 % 36.94 % 0.2 s 4 cores @ 2.5 Ghz (Python)
130 FD2 42.67 % 62.54 % 38.41 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
131 centernet 42.45 % 58.95 % 37.56 % 0.01 s GPU @ 2.5 Ghz (Python)
132 Disp R-CNN (velo)
This method uses stereo information.
code 42.25 % 58.27 % 36.90 % 0.42 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.
133 Disp R-CNN
This method uses stereo information.
code 42.23 % 58.26 % 36.88 % 0.42 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.
134 yolo_rgb 41.59 % 62.22 % 37.32 % 0.07 s GPU @ 2.5 Ghz (Python)
135 M3D-RPN code 41.54 % 61.54 % 35.23 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
136 Center3D 40.99 % 65.34 % 36.50 % 0.05 s GPU @ 3.5 Ghz (Python)
137 MV-RGBD-RF
This method makes use of Velodyne laser scans.
40.94 % 51.10 % 34.83 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
138 MTDP 40.46 % 53.83 % 35.74 % 0.15 s GPU @ 2.0 Ghz (Python)
139 Int-YOLO code 39.83 % 53.34 % 34.16 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
140 GNN 39.80 % 58.30 % 34.56 % 0.2 s 1 core @ 2.5 Ghz (Python)
141 DP3D 37.13 % 53.50 % 32.82 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
142 yolo_depth 36.89 % 50.88 % 32.64 % 0.07 s GPU @ 2.5 Ghz (Python)
143 SparsePool code 36.26 % 44.21 % 32.57 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
144 PG-MonoNet 36.09 % 47.28 % 32.15 % 0.19 s GPU @ 2.5 Ghz (Python)
145 DP3D 36.05 % 52.18 % 30.19 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
146 SS3D 35.48 % 52.97 % 31.07 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
147 DSGN
This method uses stereo information.
code 35.15 % 49.10 % 31.41 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
148 pAUCEnsT 34.90 % 50.51 % 30.35 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
149 Retinanet100 32.30 % 46.60 % 28.29 % 0.2 s 4 cores @ 2.5 Ghz (Python)
150 TopNet-Retina
This method makes use of Velodyne laser scans.
31.98 % 47.51 % 29.84 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
151 yolov3_warp 29.48 % 44.46 % 25.84 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
152 PB3D
This method uses stereo information.
28.78 % 45.05 % 25.66 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
153 OC Stereo
This method uses stereo information.
code 28.76 % 43.18 % 24.80 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
154 MMRetina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
28.00 % 43.71 % 24.62 % 0.38 s GPU @ 2.5 Ghz (Python)
155 Vote3D
This method makes use of Velodyne laser scans.
27.99 % 39.81 % 25.19 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
156 softyolo 27.90 % 41.90 % 24.74 % 0.16 s 4 cores @ 2.5 Ghz (Python)
157 LSVM-MDPM-us code 27.81 % 37.66 % 24.83 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
158 DPM-VOC+VP 27.73 % 41.58 % 24.61 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
159 100Frcnn 27.69 % 43.23 % 23.91 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
160 RefinedMPL 27.17 % 44.47 % 22.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
161 LSVM-MDPM-sv 26.05 % 35.70 % 23.56 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
162 DPM-C8B1
This method uses stereo information.
25.57 % 41.47 % 21.93 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
163 BdCost+DA+BB+MS 25.52 % 33.92 % 21.14 % TBD s 4 cores @ 2.5 Ghz (C/C++)
164 R-CNN_VGG 25.14 % 34.28 % 22.17 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
165 Lidar_ROI+Yolo(UJS) 24.42 % 36.43 % 21.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
166 RT3D-GMP
This method uses stereo information.
22.90 % 33.64 % 19.87 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
167 BdCost+DA+BB 20.00 % 26.87 % 16.76 % TBD s 4 cores @ 2.5 Ghz (C/C++)
168 mBoW
This method makes use of Velodyne laser scans.
17.63 % 26.66 % 16.02 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
169 TopNet-HighRes
This method makes use of Velodyne laser scans.
13.98 % 22.86 % 14.52 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
170 RT3DStereo
This method uses stereo information.
12.96 % 19.58 % 11.47 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
171 KD53-20 12.81 % 20.05 % 11.99 % 0.19 s 4 cores @ 2.5 Ghz (Python)
172 yyyyolo 12.52 % 16.29 % 11.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
173 TopNet-UncEst
This method makes use of Velodyne laser scans.
12.00 % 18.14 % 11.85 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
174 CBNet 0.39 % 0.24 % 0.44 % 1 s 4 cores @ 2.5 Ghz (Python)
175 softretina 0.25 % 0.16 % 0.18 % 0.16 s 4 cores @ 2.5 Ghz (Python)
176 YOLOv2 code 0.06 % 0.15 % 0.07 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
177 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.04 % 0.00 % 0.04 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
178 JSyolo 0.03 % 0.02 % 0.04 % 0.16 s 4 cores @ 2.5 Ghz (Python)
179 UM3D_TUM 0.00 % 0.00 % 0.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Object Detection and Orientation Estimation Evaluation

Cars


Method Setting Code Moderate Easy Hard Runtime Environment
1 CLOCs_PVCas 95.79 % 96.74 % 90.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
2 HUAWEI Octopus-LIS 95.06 % 96.05 % 92.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
3 SPANet 95.03 % 96.31 % 89.99 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
4 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 94.57 % 98.15 % 91.85 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
5 nonet 94.48 % 95.85 % 91.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
6 MVRA + I-FRCNN+ 94.46 % 95.66 % 81.74 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
7 SERCNN
This method makes use of Velodyne laser scans.
94.24 % 96.31 % 89.71 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
8 CVRS_PF 94.23 % 95.55 % 91.21 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
9 EPNet code 94.22 % 96.13 % 89.68 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
10 D3D 94.18 % 95.22 % 89.14 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
11 OneCoLab SicNet V2 94.15 % 95.44 % 91.66 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
12 FDet 94.07 % 95.82 % 91.54 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
13 tbd code 94.03 % 95.66 % 91.20 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
14 HRI-MSP-L
This method makes use of Velodyne laser scans.
93.82 % 95.50 % 91.27 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
15 Patches - EMP
This method makes use of Velodyne laser scans.
93.58 % 97.88 % 90.31 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
16 Associate-3Ddet_v2 93.46 % 96.66 % 88.20 % 0.04 s 1 core @ 2.5 Ghz (Python)
17 VAL 93.45 % 96.83 % 83.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
18 OAP 93.35 % 96.56 % 85.69 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
19 CLOCs_PointCas 93.34 % 96.66 % 85.87 % 0.1 s GPU @ 2.5 Ghz (Python)
20 Deep MANTA 93.31 % 98.83 % 82.95 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
21 CIA-SSD
This method makes use of Velodyne laser scans.
93.22 % 96.75 % 85.64 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
22 AIMC-RUC 93.14 % 96.64 % 87.92 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
23 ELE 93.07 % 98.42 % 90.17 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
24 MVX-Net++ 92.93 % 96.16 % 87.69 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
25 PC-RGNN 92.91 % 96.54 % 87.67 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
26 scssd-normal(0.3) 92.88 % 96.44 % 87.67 % 0.05 s GPU @ 2.5 Ghz (Python)
27 Cas-SSD 92.83 % 96.38 % 87.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 FLID 92.77 % 95.64 % 85.00 % 0.04 s GPU @ 2.5 Ghz (Python)
29 HotSpotNet 92.74 % 96.20 % 89.68 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
30 PointRCNN-deprecated
This method makes use of Velodyne laser scans.
92.74 % 96.70 % 85.51 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
31 scssd-normal(0.4) 92.71 % 96.24 % 87.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
32 IGRP 92.66 % 96.27 % 87.63 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
33 92.58 % 96.08 % 89.60 %
34 SARPNET 92.58 % 95.82 % 87.33 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
35 Patches
This method makes use of Velodyne laser scans.
92.57 % 96.31 % 87.41 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
36 R-GCN 92.53 % 96.16 % 87.45 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
37 PPFNet code 92.52 % 96.30 % 87.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
38 PI-RCNN 92.52 % 96.15 % 87.47 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
39 CenterNet3D 92.48 % 95.71 % 89.54 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
40 Discrete-PointDet 92.48 % 95.89 % 87.08 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
41 PointPainting
This method makes use of Velodyne laser scans.
92.43 % 98.36 % 89.49 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
42 3D IoU-Net 92.42 % 96.31 % 87.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
43 CLOCs_SecCas 92.37 % 95.16 % 88.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
44 92.32 % 95.83 % 89.39 %
45 VAR 92.28 % 95.08 % 89.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
46 OneCoLab SicNet 92.23 % 95.53 % 89.60 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
47 LZY_RCNN 92.18 % 93.57 % 89.61 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
48 SegVoxelNet 92.16 % 95.86 % 86.90 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
49 CP
This method makes use of Velodyne laser scans.
92.16 % 96.05 % 87.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 PointRGCN 92.15 % 97.48 % 86.83 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
51 NLK-3D 92.15 % 95.20 % 87.13 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
52 RethinkDet3D 92.04 % 95.68 % 86.97 % 0.15 s 1 core @ 2.5 Ghz (Python)
53 MDA 92.01 % 94.87 % 89.31 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
54 PVNet 92.00 % 94.82 % 89.08 % 0,1 s 1 core @ 2.5 Ghz (Python)
55 F-ConvNet
This method makes use of Velodyne laser scans.
code 91.98 % 95.81 % 79.83 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
56 TBD 91.97 % 93.46 % 89.36 % 0.05 s GPU @ 2.5 Ghz (Python)
57 PointCSE 91.95 % 95.52 % 86.75 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
58 IE-PointRCNN 91.94 % 96.00 % 86.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 91.87 % 95.86 % 86.78 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
60 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 91.77 % 95.90 % 86.92 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
61 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 91.73 % 95.00 % 88.86 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
62 Pointpillar_TV 91.61 % 94.80 % 88.25 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
63 C-GCN 91.57 % 95.63 % 86.13 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
64 tt code 91.38 % 95.14 % 88.39 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
65 CU-PointRCNN 91.25 % 97.24 % 86.85 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
66 RUC 91.25 % 95.01 % 88.14 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
67 deprecated 91.18 % 96.19 % 83.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
68 3DBN_2 91.05 % 94.89 % 88.42 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
69 deprecated 91.02 % 94.06 % 78.56 % 0.05 s GPU @ 2.0 Ghz (Python)
70 Mono3CN 90.96 % 94.22 % 82.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 HRI-VoxelFPN 90.76 % 96.35 % 85.37 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
72 SSL-RTM3D 90.70 % 96.34 % 80.72 % 0.03 s 1 core @ 2.5 Ghz (Python)
73 anonymous 90.70 % 96.46 % 82.39 % 1 s 1 core @ 2.5 Ghz (C/C++)
74 PointPillars
This method makes use of Velodyne laser scans.
code 90.70 % 93.84 % 87.47 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
75 WS3D
This method makes use of Velodyne laser scans.
90.69 % 94.85 % 85.94 % 0.1 s GPU @ 2.5 Ghz (Python)
76 CentrNet-v1
This method makes use of Velodyne laser scans.
90.48 % 93.79 % 87.43 % 0.03 s GPU @ 2.5 Ghz (Python)
77 DDB
This method makes use of Velodyne laser scans.
90.38 % 93.21 % 86.42 % 0.05 s GPU @ 2.5 Ghz (Python)
78 OACV 90.35 % 93.95 % 81.90 % 0.23 s GPU @ 2.5 Ghz (Python)
79 autonet 90.31 % 93.30 % 87.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
80 MVSLN 90.26 % 95.95 % 82.75 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
81 3D IoU Loss
This method makes use of Velodyne laser scans.
90.21 % 95.60 % 84.96 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
82 Bit 90.19 % 93.42 % 86.48 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
83 ARPNET 90.11 % 93.42 % 82.56 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
84 TANet code 90.11 % 93.52 % 84.61 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
85 EPENet 90.09 % 93.83 % 86.76 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
86 SFB-SECOND 90.04 % 95.99 % 84.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
87 CentrNet-FG 90.04 % 93.51 % 87.02 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
88 CG-Stereo
This method uses stereo information.
89.98 % 96.28 % 82.21 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
89 Sogo_MM 89.97 % 94.15 % 79.94 % 1.5 s GPU @ 2.5 Ghz (C/C++)
90 P3D 89.93 % 94.07 % 83.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
91 Deep3DBox 89.88 % 94.62 % 76.40 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
92 SECOND-V1.5
This method makes use of Velodyne laser scans.
code 89.88 % 95.53 % 84.46 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
93 PointPiallars_SECA 89.86 % 92.96 % 86.46 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
94 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
89.82 % 93.37 % 85.67 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
95 VOXEL_FPN_HR 89.81 % 93.52 % 84.59 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
96 BVVF 89.77 % 95.55 % 84.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
97 baseline 89.69 % 92.61 % 86.03 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
98 GPP code 89.68 % 93.94 % 80.60 % 0.23 s GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. arXiv preprint arXiv:1811.06666 2018.
99 SubCNN 89.53 % 94.11 % 79.14 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
100 FCY
This method makes use of Velodyne laser scans.
89.49 % 93.02 % 85.72 % 0.02 s GPU @ 2.5 Ghz (Python)
101 SAANet 89.46 % 95.64 % 82.12 % 0.10 s 1 core @ 2.5 Ghz (Python)
102 SCNet
This method makes use of Velodyne laser scans.
89.36 % 95.23 % 84.03 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
103 RUC code 89.26 % 92.28 % 85.38 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
104 AVOD
This method makes use of Velodyne laser scans.
code 89.22 % 94.98 % 82.14 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
105 IAFA 89.14 % 92.96 % 79.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
106 MCA 88.91 % 92.91 % 79.11 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
107 RUC code 88.90 % 92.68 % 84.04 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
108 AVOD-FPN
This method makes use of Velodyne laser scans.
code 88.61 % 94.65 % 83.71 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
109 SS3D_HW 88.50 % 94.45 % 68.61 % 0.4 s GPU @ 2.5 Ghz (Python)
110 PSMD 88.29 % 93.59 % 75.35 % 0.1 s GPU @ 2.5 Ghz (Python)
111 Prune 88.10 % 93.86 % 80.41 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
112 autoRUC 88.03 % 93.80 % 80.36 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
113 AACL 88.00 % 93.36 % 73.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
114 PointRes
This method makes use of Velodyne laser scans.
This method makes use of GPS/IMU information.
This is an online method (no batch processing).
87.83 % 95.24 % 83.39 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
115 DeepStereoOP 87.81 % 93.68 % 77.60 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
116 3DBN
This method makes use of Velodyne laser scans.
87.59 % 93.34 % 79.91 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
117 FQNet 87.49 % 93.66 % 73.61 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
118 Shift R-CNN (mono) code 87.47 % 93.75 % 77.19 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
119 PP-3D 87.46 % 93.09 % 79.88 % 0.1 s 1 core @ 2.5 Ghz (Python)
120 MonoPSR code 87.45 % 93.29 % 72.26 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
121 Mono3D code 87.28 % 93.13 % 77.00 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
122 MA 87.08 % 93.12 % 79.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 3DNN 87.08 % 93.78 % 79.72 % 0.09 s GPU @ 2.5 Ghz (Python)
124 SMOKE code 87.02 % 92.94 % 77.12 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
125 MonoSS 86.95 % 92.88 % 77.04 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
126 3DOP
This method uses stereo information.
code 86.93 % 91.31 % 76.72 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
127 CDN
This method uses stereo information.
86.90 % 95.79 % 79.05 % 0.6 s GPU @ 2.5 Ghz (Python)
128 RTM3D code 86.73 % 91.75 % 77.18 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
129 IMA 86.71 % 92.51 % 76.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
130 voxelrcnn 86.61 % 94.59 % 79.80 % 15 s 1 core @ 2.5 Ghz (C/C++)
131 MBR-SSD 86.57 % 90.97 % 78.03 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
132 MonoPair 86.11 % 91.65 % 76.45 % 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.
133 DSGN
This method uses stereo information.
code 86.03 % 95.42 % 78.27 % 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.
134 tiny-stereo-volume 85.52 % 94.53 % 77.54 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
135 NL_M3D 85.32 % 90.88 % 70.87 % 0.2 s 1 core @ 2.5 Ghz (Python)
136 StereoFENet
This method uses stereo information.
85.14 % 91.28 % 76.80 % 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.
137 PB3D
This method uses stereo information.
84.75 % 95.15 % 75.34 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
138 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 84.42 % 94.83 % 76.95 % 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.
139 SS3D 84.38 % 92.57 % 69.82 % 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.
140 IDA-3D
This method uses stereo information.
84.32 % 92.63 % 73.98 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
141 CDN-PL++
This method uses stereo information.
84.21 % 94.45 % 76.69 % 0.4 s GPU @ 2.5 Ghz (C/C++)
142 UDI-mono3D 84.20 % 91.88 % 75.38 % 0.05 s 1 core @ 2.5 Ghz (Python)
143 MonoFENet 84.09 % 91.42 % 75.93 % 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.
144 SECA 83.99 % 92.34 % 78.85 % 1 s GPU @ 2.5 Ghz (Python)
145 Complexer-YOLO
This method makes use of Velodyne laser scans.
83.89 % 91.77 % 79.24 % 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.
146 ZoomNet
This method uses stereo information.
code 83.79 % 94.14 % 68.78 % 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.
147 seivl 83.38 % 90.32 % 81.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
148 M3D-RPN code 82.81 % 88.38 % 67.08 % 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 .
149 MTMono3d 82.65 % 90.34 % 74.98 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
150 RAR-Net 82.63 % 88.40 % 66.90 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
151 Center3D 82.51 % 93.10 % 70.79 % 0.05 s GPU @ 3.5 Ghz (Python)
152 SSL-RTM3D Res18 82.43 % 93.13 % 72.47 % 0.02 s GPU @ 2.5 Ghz (Python)
153 ASOD 82.13 % 93.56 % 67.32 % 0.28 s GPU @ 2.5 Ghz (Python)
154 D4LCN code 82.08 % 90.01 % 63.98 % 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.
155 deprecated 81.99 % 92.07 % 67.48 % 1 core @ 2.5 Ghz (C/C++)
156 S3D 81.93 % 91.59 % 67.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 Pseudo-LiDAR++
This method uses stereo information.
code 81.87 % 94.14 % 74.29 % 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.
158 LNET 81.81 % 91.36 % 67.33 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
159 Disp R-CNN
This method uses stereo information.
code 81.70 % 93.02 % 67.16 % 0.42 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.
160 Disp R-CNN (velo)
This method uses stereo information.
code 81.67 % 92.86 % 67.22 % 0.42 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.
161 Pseudo-LiDAR E2E
This method uses stereo information.
81.56 % 93.74 % 74.23 % 0.4 s GPU @ 2.5 Ghz (Python)
162 HG-Mono 81.53 % 88.76 % 63.12 % 0.46 s GPU @ 2.5 Ghz (C/C++)
163 HR-SECOND code 81.23 % 88.32 % 74.89 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
164 BS3D 81.22 % 94.66 % 68.39 % 22 ms Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
165 DP3D 81.07 % 87.49 % 65.12 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
166 LCD3D 81.01 % 91.20 % 64.29 % 0.03 s GPU @ 2.5 Ghz (Python)
167 Stereo3D
This method uses stereo information.
80.88 % 93.65 % 61.17 % 0.1 s GPU 1080Ti
168 DP3D 80.87 % 87.58 % 64.88 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
169 FRCNN+Or code 80.57 % 91.50 % 67.49 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
170 UM3D_TUM 80.15 % 92.80 % 65.77 % 0.05 s 1 core @ 2.5 Ghz (Python)
171 YoloMono3D 78.50 % 91.43 % 58.80 % 0.05 s GPU @ 2.5 Ghz (Python)
172 3D-GCK 78.44 % 88.59 % 66.28 % 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.
173 3D-SSMFCNN code 77.82 % 77.84 % 68.67 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
174 DA-3Ddet 77.73 % 89.01 % 61.48 % 0.4 s GPU @ 2.5 Ghz (Python)
175 ITS-MDPL 76.89 % 92.05 % 69.53 % 0.16 s GPU @ 2.5 Ghz (Python)
176 3DVP code 75.71 % 84.44 % 64.41 % 40 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
177 GS3D 75.63 % 85.79 % 61.85 % 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.
178 Pose-RCNN 75.41 % 89.49 % 63.57 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
179 avodC 75.35 % 86.76 % 70.17 % 0.1 s GPU @ 2.5 Ghz (Python)
180 SubCat code 75.26 % 83.31 % 59.55 % 0.7 s 6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
181 3D FCN
This method makes use of Velodyne laser scans.
74.54 % 86.65 % 67.73 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
182 OC Stereo
This method uses stereo information.
code 73.34 % 86.86 % 61.37 % 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.
183 BdCost+DA+BB+MS 72.87 % 84.39 % 57.07 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
184 BdCost+DA+MS 72.65 % 84.06 % 58.08 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
185 BdCost+DA+BB 70.07 % 84.66 % 55.50 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
186 ROI-10D 68.14 % 75.32 % 58.98 % 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.
187 BirdNet+
This method makes use of Velodyne laser scans.
code 67.65 % 91.82 % 65.11 % 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. arXiv:2003.04188 [cs.CV] 2020.
188 multi-task CNN 67.51 % 79.00 % 58.80 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
189 Decoupled-3D v2 67.47 % 88.23 % 54.04 % 0.08 s GPU @ 2.5 Ghz (C/C++)
190 Decoupled-3D 67.23 % 87.34 % 53.84 % 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.
191 BdCost48LDCF code 65.50 % 80.44 % 51.24 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
192 OC-DPM 65.32 % 77.35 % 51.00 % 10 s 8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
193 deprecated 65.30 % 69.02 % 63.66 % 0.05 s GPU @ >3.5 Ghz (Python)
194 3DVSSD 65.28 % 79.56 % 55.73 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
195 RefinedMPL 64.02 % 87.95 % 52.06 % 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.
196 BdCost48-25C 63.90 % 80.69 % 51.54 % 4 s 1 core @ 2.5 Ghz (C/C++)
197 DPM-VOC+VP 63.58 % 79.09 % 46.59 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
198 AOG-View 62.62 % 77.62 % 48.27 % 3 s 1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
199 PG-MonoNet 61.20 % 70.34 % 52.59 % 0.19 s GPU @ 2.5 Ghz (Python)
200 monoref3d 58.30 % 77.65 % 46.90 % 0.1 s 1 core @ 2.5 Ghz (Python)
201 ref3D 58.30 % 77.65 % 46.90 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
202 LSVM-MDPM-sv 57.48 % 70.23 % 42.54 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
203 SAMME48LDCF code 57.26 % 76.28 % 43.55 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
204 deprecated 57.01 % 62.54 % 54.94 % - -
205 BirdNet
This method makes use of Velodyne laser scans.
56.94 % 79.20 % 54.88 % 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.
206 ref3D 56.49 % 77.52 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (Python)
207 DEFT 51.66 % 57.41 % 50.02 % 1 s GPU @ 2.5 Ghz (Python)
208 VeloFCN
This method makes use of Velodyne laser scans.
51.05 % 70.03 % 44.82 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
209 Mono3D_PLiDAR code 49.39 % 76.90 % 41.13 % 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.
210 DPM-C8B1
This method uses stereo information.
48.00 % 57.76 % 35.52 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
211 LTN 46.54 % 48.96 % 41.58 % 0.4 s GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
212 sensekitti code 46.12 % 49.16 % 42.79 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
213 ReSqueeze 45.58 % 49.08 % 41.33 % 0.03 s GPU @ >3.5 Ghz (Python)
214 Kinematic3D code 45.50 % 58.33 % 34.81 % 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 .
215 Resnet101Faster rcnn 44.01 % 51.21 % 39.19 % 1 s 1 core @ 2.5 Ghz (Python)
216 anonymous 40.75 % 45.00 % 34.48 % 1 s 1 core @ 2.5 Ghz (C/C++)
217 Chovy 40.34 % 41.64 % 38.31 % 0.04 s GPU @ 2.5 Ghz (Python)
218 cvMax 40.31 % 41.97 % 37.57 % 0.04 s GPU @ >3.5 Ghz (Python)
219 deprecated 40.03 % 40.31 % 37.35 % 0.04 s GPU @ 2.5 Ghz (Python)
220 3D-CVF at SPA
This method makes use of Velodyne laser scans.
39.79 % 40.44 % 36.10 % 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.
221 HR-faster-rcnn 39.35 % 39.78 % 36.47 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
222 deprecated 38.89 % 40.49 % 35.13 % 0.06 s GPU @ >3.5 Ghz (Python)
223 FD2 38.89 % 48.29 % 34.35 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
224 dgist_multiDetNet 38.76 % 39.75 % 35.38 % 0.08 s GPU Titanx Pascal (Python)
225 bin 38.58 % 43.36 % 32.42 % 15ms s GPU @ >3.5 Ghz (Python)
226 PVF-NET 38.53 % 39.57 % 38.23 % 0.1 s 1 core @ 2.5 Ghz (Python)
227 DGIST-CellBox 38.36 % 39.11 % 36.15 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
228 SA-SSD code 38.30 % 39.40 % 37.07 % 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.
229 Faster RCNN + A 37.92 % 39.50 % 33.85 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
230 KNN-GCNN 37.80 % 38.80 % 36.52 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
231 JSU-NET 37.60 % 41.33 % 33.41 % 0.1 s 1 core @ 2.5 Ghz (Python)
232 Faster RCNN + G 37.49 % 39.05 % 33.40 % 1.1 s GPU @ 2.5 Ghz (Python)
233 Faster RCNN + A 37.35 % 38.75 % 33.38 % 0.19 s GPU @ 2.5 Ghz (Python)
234 yolo4 37.27 % 38.19 % 32.45 % 0.02 s 1 core @ 2.5 Ghz (Python)
235 Point-GNN
This method makes use of Velodyne laser scans.
code 37.20 % 38.66 % 36.29 % 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.
236 F-3DNet 37.18 % 38.58 % 36.44 % 0.5 s GPU @ 2.5 Ghz (Python)
237 yolo4_5l 37.10 % 36.95 % 33.62 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
238 GAFM 37.08 % 40.28 % 33.08 % 0.5 s 1 core @ 2.5 Ghz (Python)
239 CRCNNA 37.04 % 40.19 % 32.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
240 Faster RCNN + Gr + A 36.95 % 38.22 % 33.16 % 1.29 s GPU @ 2.5 Ghz (Python)
241 CSFADet 36.83 % 39.76 % 32.73 % 0.05 s GPU @ 2.5 Ghz (Python)
242 yolo4_5l code 36.81 % 37.14 % 33.24 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
243 cas_retina 36.63 % 39.70 % 31.52 % 0.2 s 4 cores @ 2.5 Ghz (Python)
244 GA_BALANCE 36.62 % 38.44 % 31.94 % 1 s 1 core @ 2.5 Ghz (Python)
245 GA_rpn500 36.54 % 38.33 % 32.67 % 1 s 1 core @ 2.5 Ghz (Python)
246 GA2500 36.54 % 38.33 % 32.67 % 0.2 s 1 core @ 2.5 Ghz (Python)
247 cas+res+soft 36.53 % 38.82 % 32.26 % 0.2 s 4 cores @ 2.5 Ghz (Python)
248 merge12-12 36.47 % 38.83 % 32.20 % 0.2 s 4 cores @ 2.5 Ghz (Python)
249 GA_FULLDATA 36.43 % 38.90 % 31.61 % 1 s 4 cores @ 2.5 Ghz (Python)
250 AtrousDet 36.36 % 38.86 % 31.79 % 0.05 s TITAN X
251 bigger_ga 36.21 % 38.41 % 31.58 % 1 s 1 core @ 2.5 Ghz (Python)
252 Scan_YOLO 36.02 % 36.78 % 32.65 % 0.1 s 4 cores @ 3.0 Ghz (Python)
253 cas_retina_1_13 35.89 % 39.02 % 31.33 % 0.03 s 4 cores @ 2.5 Ghz (Python)
254 cascadercnn 35.61 % 36.22 % 30.16 % 0.36 s 4 cores @ 2.5 Ghz (Python)
255 yolo_rgb 35.23 % 36.60 % 31.70 % 0.07 s GPU @ 2.5 Ghz (Python)
256 Cmerge 35.02 % 38.33 % 29.06 % 0.2 s 4 cores @ 2.5 Ghz (Python)
257 ga50 34.95 % 38.21 % 30.29 % 1 s 1 core @ 2.5 Ghz (Python)
258 softretina 34.57 % 39.31 % 29.27 % 0.16 s 4 cores @ 2.5 Ghz (Python)
259 Retinanet100 34.37 % 39.15 % 28.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
260 ZKNet 34.27 % 38.09 % 29.93 % 0.01 s GPU @ 2.0 Ghz (Python)
261 bifpn_fsrn 33.84 % 37.56 % 29.98 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
262 LPN 33.61 % 34.57 % 29.72 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
263 cascade_gw 33.53 % 34.76 % 29.71 % 0.2 s 4 cores @ 2.5 Ghz (Python)
264 RADNet-Fusion
This method makes use of Velodyne laser scans.
33.31 % 31.96 % 32.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
265 RADNet-LIDAR
This method makes use of Velodyne laser scans.
33.08 % 31.30 % 32.31 % 0.1 s 1 core @ 2.5 Ghz (Python)
266 SceneNet 32.78 % 37.79 % 28.30 % 0.03 s GPU @ 2.5 Ghz (C/C++)
267 MTDP 32.68 % 36.06 % 27.12 % 0.15 s GPU @ 2.0 Ghz (Python)
268 CBNet 32.63 % 36.51 % 29.26 % 1 s 4 cores @ 2.5 Ghz (Python)
269 Fast-SSD 32.51 % 41.41 % 28.45 % 0.06 s GTX650Ti
270 centernet 32.22 % 35.79 % 28.50 % 0.01 s GPU @ 2.5 Ghz (Python)
271 RFCN_RFB 32.06 % 35.39 % 27.94 % 0.2 s 4 cores @ 2.5 Ghz (Python)
272 MTNAS 31.15 % 35.43 % 27.02 % 0.02 s 1 core @ 2.5 Ghz (python)
273 yolo800 31.13 % 32.49 % 26.76 % 0.13 s 4 cores @ 2.5 Ghz (Python)
274 VoxelNet(Unofficial) 31.08 % 34.54 % 28.79 % 0.5 s GPU @ 2.0 Ghz (Python)
275 SAIC-SA-3D
This method makes use of Velodyne laser scans.
31.02 % 41.38 % 29.60 % 0.05 s GPU @ 2.5 Ghz (Python)
276 RFCN 30.93 % 34.24 % 25.27 % 0.2 s 4 cores @ 2.5 Ghz (Python)
277 yolo_depth 30.33 % 36.32 % 26.80 % 0.07 s GPU @ 2.5 Ghz (Python)
278 AOG code 29.81 % 33.28 % 23.91 % 3 s 4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
279 m-prcnn
This method uses stereo information.
29.62 % 34.80 % 22.79 % 0.43 s 1 core @ 2.5 Ghz (Python)
280 DAM 28.97 % 37.05 % 25.28 % 1 s GPU @ 2.5 Ghz (Python)
281 fasterrcnn 28.42 % 30.28 % 24.95 % 0.2 s 4 cores @ 2.5 Ghz (Python)
282 RFBnet 27.91 % 34.44 % 25.24 % 0.2 s 4 cores @ 2.5 Ghz (Python)
283 SubCat48LDCF code 26.68 % 34.33 % 19.44 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
284 Lidar_ROI+Yolo(UJS) 25.33 % 30.36 % 22.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
285 RADNet-Mono 24.78 % 28.55 % 22.84 % 0.1 s 1 core @ 2.5 Ghz (Python)
286 RT3D-GMP
This method uses stereo information.
24.27 % 28.33 % 18.51 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
287 100Frcnn 23.32 % 32.81 % 19.45 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
288 RT3DStereo
This method uses stereo information.
21.41 % 25.58 % 17.52 % 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.
289 CSoR
This method makes use of Velodyne laser scans.
code 20.82 % 30.65 % 17.14 % 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.
290 RT3D
This method makes use of Velodyne laser scans.
18.96 % 24.41 % 19.85 % 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.
291 softyolo 18.31 % 26.80 % 15.28 % 0.16 s 4 cores @ 2.5 Ghz (Python)
292 Licar
This method makes use of Velodyne laser scans.
16.16 % 18.56 % 15.59 % 0.09 s GPU @ 2.0 Ghz (Python)
293 VoxelJones code 15.41 % 17.83 % 14.13 % .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.
294 KD53-20 13.76 % 20.58 % 11.91 % 0.19 s 4 cores @ 2.5 Ghz (Python)
295 MuRF 1.75 % 0.63 % 2.14 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
296 MP 1.51 % 0.63 % 2.03 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
297 PiP 1.45 % 0.56 % 1.85 % 0.033 s 1 core @ 2.5 Ghz (Python)
298 Simple3D Net 1.38 % 0.63 % 1.76 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
299 SPA 1.25 % 0.59 % 1.64 % 0.1 s 1 core @ 2.5 Ghz (Python)
300 Associate-3Ddet code 1.20 % 0.52 % 1.38 % 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.
301 JSyolo 0.00 % 0.00 % 0.00 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


Pedestrians


Method Setting Code Moderate Easy Hard Runtime Environment
1 VMVS
This method makes use of Velodyne laser scans.
68.19 % 79.98 % 63.18 % 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.
2 Sogo_MM 67.31 % 80.02 % 61.99 % 1.5 s GPU @ 2.5 Ghz (C/C++)
3 SubCNN 66.70 % 79.65 % 61.35 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
4 F-ConvNet
This method makes use of Velodyne laser scans.
code 63.87 % 75.19 % 58.57 % 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.
5 3DOP
This method uses stereo information.
code 61.48 % 74.22 % 55.89 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
6 HotSpotNet 60.65 % 70.36 % 57.42 % 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.
7 DeepStereoOP 60.15 % 73.76 % 55.30 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
8 Pose-RCNN 59.84 % 76.24 % 53.59 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
9 Mono3CN 59.17 % 72.16 % 53.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 FFNet code 58.87 % 69.24 % 53.75 % 1.07 s GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
11 Mono3D code 58.66 % 71.19 % 53.94 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
12 58.45 % 67.82 % 55.33 %
13 56.89 % 66.97 % 52.75 %
14 MVX-Net++ 54.86 % 64.23 % 50.85 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
15 RethinkDet3D 54.72 % 63.97 % 50.72 % 0.15 s 1 core @ 2.5 Ghz (Python)
16 MonoPSR code 54.65 % 68.98 % 50.07 % 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.
17 FDet 53.35 % 63.39 % 50.42 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
18 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 52.42 % 63.45 % 49.23 % 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.
19 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 52.20 % 63.51 % 48.27 % 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.
20 FRCNN+Or code 52.15 % 67.03 % 47.14 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
21 PointPainting
This method makes use of Velodyne laser scans.
50.22 % 59.25 % 46.95 % 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.
22 ARPNET 48.49 % 60.47 % 45.02 % 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.
23 3DBN_2 48.43 % 59.19 % 45.73 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
24 TBD 48.34 % 58.57 % 44.85 % 0.05 s GPU @ 2.5 Ghz (Python)
25 PointPillars
This method makes use of Velodyne laser scans.
code 48.05 % 57.47 % 45.40 % 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.
26 PPFNet code 47.73 % 55.78 % 44.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
27 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 47.33 % 57.19 % 44.31 % 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.
28 PVNet 46.68 % 57.18 % 44.38 % 0,1 s 1 core @ 2.5 Ghz (Python)
29 Shift R-CNN (mono) code 46.56 % 64.73 % 41.86 % 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.
30 VOXEL_FPN_HR 45.65 % 56.17 % 42.10 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
31 UDI-mono3D 44.75 % 57.42 % 40.21 % 0.05 s 1 core @ 2.5 Ghz (Python)
32 HWFD 44.66 % 48.89 % 42.14 % 0.21 s one 1080Ti
33 SS3D_HW 44.43 % 59.56 % 38.77 % 0.4 s GPU @ 2.5 Ghz (Python)
34 AVOD-FPN
This method makes use of Velodyne laser scans.
code 43.99 % 53.48 % 41.56 % 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.
35 Disp R-CNN (velo)
This method uses stereo information.
code 43.99 % 60.06 % 39.79 % 0.42 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.
36 DGIST-CellBox 43.86 % 48.68 % 41.52 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
37 Disp R-CNN
This method uses stereo information.
code 43.76 % 60.00 % 39.55 % 0.42 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.
38 dgist_multiDetNet 43.48 % 49.02 % 40.97 % 0.08 s GPU Titanx Pascal (Python)
39 DDB
This method makes use of Velodyne laser scans.
43.21 % 52.02 % 40.81 % 0.05 s GPU @ 2.5 Ghz (Python)
40 PiP 42.76 % 51.23 % 40.06 % 0.033 s 1 core @ 2.5 Ghz (Python)
41 MonoPair 42.38 % 55.26 % 38.53 % 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.
42 NLK-3D 41.71 % 54.22 % 39.32 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
43 MTMono3d 41.63 % 54.28 % 36.32 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
44 Faster RCNN + Gr + A 40.92 % 47.81 % 37.89 % 1.29 s GPU @ 2.5 Ghz (Python)
45 HR-SECOND code 40.81 % 51.12 % 37.48 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
46 Faster RCNN + G 40.49 % 47.16 % 37.57 % 1.1 s GPU @ 2.5 Ghz (Python)
47 Faster RCNN + A 39.95 % 47.52 % 37.08 % 0.19 s GPU @ 2.5 Ghz (Python)
48 CentrNet-FG 39.88 % 47.51 % 37.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
49 CentrNet-v1
This method makes use of Velodyne laser scans.
39.83 % 46.21 % 38.05 % 0.03 s GPU @ 2.5 Ghz (Python)
50 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 39.76 % 50.30 % 36.90 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
51 SS3D 39.60 % 53.72 % 35.40 % 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.
52 Faster RCNN + A 39.44 % 46.80 % 36.46 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
53 HR-faster-rcnn 39.02 % 47.41 % 35.57 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
54 Center3D 38.59 % 53.15 % 34.77 % 0.05 s GPU @ 3.5 Ghz (Python)
55 CSFADet 38.41 % 46.75 % 35.44 % 0.05 s GPU @ 2.5 Ghz (Python)
56 DPM-VOC+VP 37.79 % 52.91 % 33.27 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
57 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
37.23 % 44.01 % 35.54 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
58 CG-Stereo
This method uses stereo information.
36.47 % 48.23 % 32.77 % 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.
59 PP-3D 36.22 % 44.49 % 34.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
60 TANet code 36.21 % 42.54 % 34.39 % 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.
61 SAANet 36.08 % 46.09 % 34.14 % 0.10 s 1 core @ 2.5 Ghz (Python)
62 AtrousDet 35.85 % 44.79 % 32.12 % 0.05 s TITAN X
63 Stereo3D
This method uses stereo information.
35.62 % 48.99 % 31.58 % 0.1 s GPU 1080Ti
64 SCNet
This method makes use of Velodyne laser scans.
35.49 % 44.50 % 33.38 % 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.
65 NL_M3D 35.20 % 46.64 % 30.56 % 0.2 s 1 core @ 2.5 Ghz (Python)
66 CRCNNA 34.88 % 43.18 % 31.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 FCY
This method makes use of Velodyne laser scans.
34.67 % 40.75 % 33.00 % 0.02 s GPU @ 2.5 Ghz (Python)
68 sensekitti code 34.26 % 41.03 % 31.51 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
69 Pointpillar_TV 34.24 % 42.95 % 32.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
70 merge12-12 34.10 % 43.60 % 30.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
71 cas+res+soft 34.01 % 43.51 % 30.28 % 0.2 s 4 cores @ 2.5 Ghz (Python)
72 cas_retina 33.98 % 43.80 % 31.12 % 0.2 s 4 cores @ 2.5 Ghz (Python)
73 cas_retina_1_13 33.87 % 43.55 % 30.99 % 0.03 s 4 cores @ 2.5 Ghz (Python)
74 D4LCN code 33.62 % 46.73 % 28.71 % 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.
75 JSU-NET 33.55 % 45.79 % 30.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
76 DP3D 33.35 % 46.50 % 29.89 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
77 SparsePool code 33.35 % 43.86 % 29.99 % 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.
78 SparsePool code 33.29 % 43.52 % 30.01 % 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.
79 LSVM-MDPM-sv 33.01 % 45.60 % 29.27 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
80 DP3D 32.99 % 44.19 % 28.19 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
81 cascadercnn 32.59 % 43.37 % 29.73 % 0.36 s 4 cores @ 2.5 Ghz (Python)
82 ReSqueeze 32.47 % 38.49 % 30.04 % 0.03 s GPU @ >3.5 Ghz (Python)
83 AVOD
This method makes use of Velodyne laser scans.
code 32.19 % 42.54 % 29.09 % 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.
84 Complexer-YOLO
This method makes use of Velodyne laser scans.
32.13 % 37.32 % 28.94 % 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.
85 yolo800 32.12 % 40.53 % 28.83 % 0.13 s 4 cores @ 2.5 Ghz (Python)
86 RPN+BF code 32.12 % 41.19 % 28.83 % 0.6 s GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
87 bin 31.94 % 36.94 % 29.50 % 15ms s GPU @ >3.5 Ghz (Python)
88 KNN-GCNN 31.91 % 39.25 % 29.76 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
89 M3D-RPN code 31.88 % 44.33 % 28.55 % 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 .
90 Point-GNN
This method makes use of Velodyne laser scans.
code 31.86 % 39.16 % 29.65 % 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.
91 HG-Mono 31.62 % 43.63 % 28.33 % 0.46 s GPU @ 2.5 Ghz (C/C++)
92 SubCat 31.26 % 42.31 % 27.39 % 1.2 s 6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
93 ZKNet 31.21 % 39.55 % 28.61 % 0.01 s GPU @ 2.0 Ghz (Python)
94 RFCN 30.97 % 40.51 % 27.45 % 0.2 s 4 cores @ 2.5 Ghz (Python)
95 LPN 30.84 % 38.60 % 28.49 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
96 DAM 30.58 % 41.32 % 27.84 % 1 s GPU @ 2.5 Ghz (Python)
97 CHTTL MMF 30.45 % 41.08 % 27.57 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
98 yolo4 30.09 % 40.84 % 27.35 % 0.02 s 1 core @ 2.5 Ghz (Python)
99 yolo4_5l 30.06 % 40.79 % 26.21 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
100 PB3D
This method uses stereo information.
30.02 % 40.27 % 26.86 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
101 RFCN_RFB 29.91 % 38.71 % 26.50 % 0.2 s 4 cores @ 2.5 Ghz (Python)
102 deprecated 29.74 % 37.71 % 27.25 % 0.05 s GPU @ 2.0 Ghz (Python)
103 PG-MonoNet 29.56 % 37.28 % 26.48 % 0.19 s GPU @ 2.5 Ghz (Python)
104 BirdNet+
This method makes use of Velodyne laser scans.
code 29.56 % 36.76 % 28.10 % 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. arXiv:2003.04188 [cs.CV] 2020.
105 fasterrcnn 29.48 % 38.63 % 26.89 % 0.2 s 4 cores @ 2.5 Ghz (Python)
106 yolo4_5l code 28.60 % 38.95 % 25.97 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
107 FD2 28.40 % 35.59 % 25.75 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
108 MTDP 28.24 % 37.49 % 25.57 % 0.15 s GPU @ 2.0 Ghz (Python)
109 yolo_depth 28.06 % 38.75 % 25.37 % 0.07 s GPU @ 2.5 Ghz (Python)
110 centernet 27.53 % 37.41 % 24.35 % 0.01 s GPU @ 2.5 Ghz (Python)
111 yolo_rgb 26.85 % 35.91 % 24.37 % 0.07 s GPU @ 2.5 Ghz (Python)
112 cascade_gw 26.32 % 36.41 % 23.73 % 0.2 s 4 cores @ 2.5 Ghz (Python)
113 Cmerge 25.09 % 34.53 % 22.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
114 DSGN
This method uses stereo information.
code 24.32 % 31.21 % 23.09 % 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.
115 ACF 24.31 % 32.23 % 21.70 % 1 s 1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
116 Resnet101Faster rcnn 23.70 % 30.19 % 21.55 % 1 s 1 core @ 2.5 Ghz (Python)
117 multi-task CNN 22.80 % 30.30 % 20.47 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
118 ACF-MR 22.61 % 29.23 % 20.08 % 0.6 s 1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
119 OC Stereo
This method uses stereo information.
code 22.02 % 31.36 % 20.20 % 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.
120 BirdNet
This method makes use of Velodyne laser scans.
21.83 % 27.12 % 20.56 % 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.
121 Retinanet100 21.71 % 29.72 % 19.12 % 0.2 s 4 cores @ 2.5 Ghz (Python)
122 softyolo 21.56 % 30.46 % 20.01 % 0.16 s 4 cores @ 2.5 Ghz (Python)
123 RT3D-GMP
This method uses stereo information.
20.81 % 29.49 % 18.34 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
124 Lidar_ROI+Yolo(UJS) 19.43 % 26.83 % 17.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
125 KD53-20 19.36 % 25.10 % 17.54 % 0.19 s 4 cores @ 2.5 Ghz (Python)
126 DPM-C8B1
This method uses stereo information.
19.17 % 27.79 % 16.48 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
127 RefinedMPL 17.26 % 25.83 % 15.41 % 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.
128 RT3DStereo
This method uses stereo information.
15.34 % 21.41 % 13.23 % 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.
129 100Frcnn 12.37 % 19.41 % 10.92 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
130 Simple3D Net 11.95 % 13.63 % 11.68 % 0.02 s GPU @ 2.5 Ghz (Python)
131 MP 5.39 % 6.41 % 5.14 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
132 CBNet 0.72 % 0.56 % 0.75 % 1 s 4 cores @ 2.5 Ghz (Python)
133 softretina 0.13 % 0.10 % 0.14 % 0.16 s 4 cores @ 2.5 Ghz (Python)
134 JSyolo 0.06 % 0.11 % 0.07 % 0.16 s 4 cores @ 2.5 Ghz (Python)
135 UM3D_TUM 0.00 % 0.00 % 0.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


Cyclists


Method Setting Code Moderate Easy Hard Runtime Environment
1 HRI-MSP-L
This method makes use of Velodyne laser scans.
82.89 % 91.97 % 75.38 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
2 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 79.70 % 86.43 % 72.96 % 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.
3 HotSpotNet 78.31 % 85.79 % 71.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
4 78.23 % 85.65 % 71.60 %
5 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 77.52 % 88.70 % 70.41 % 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.
6 PointPainting
This method makes use of Velodyne laser scans.
76.92 % 87.33 % 68.21 % 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.
7 TBD 76.79 % 87.00 % 70.00 % 0.05 s GPU @ 2.5 Ghz (Python)
8 F-ConvNet
This method makes use of Velodyne laser scans.
code 76.71 % 86.39 % 66.92 % 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.
9 75.59 % 83.45 % 68.42 %
10 VOXEL_FPN_HR 74.77 % 87.41 % 68.16 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
11 MVX-Net++ 74.65 % 86.53 % 67.43 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
12 RethinkDet3D 74.33 % 88.54 % 65.20 % 0.15 s 1 core @ 2.5 Ghz (Python)
13 3DBN_2 73.69 % 87.96 % 66.91 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
14 FDet 73.22 % 85.17 % 66.44 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
15 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 72.81 % 85.94 % 65.84 % 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.
16 PiP 71.10 % 82.83 % 64.88 % 0.033 s 1 core @ 2.5 Ghz (Python)
17 PVNet 70.50 % 83.44 % 64.47 % 0,1 s 1 core @ 2.5 Ghz (Python)
18 NLK-3D 70.10 % 85.69 % 63.27 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
19 HR-SECOND code 69.60 % 82.42 % 62.47 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
20 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 69.54 % 82.18 % 62.98 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
21 ARPNET 68.72 % 82.61 % 62.00 % 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.
22 PointPillars
This method makes use of Velodyne laser scans.
code 68.55 % 83.79 % 61.71 % 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.
23 CentrNet-FG 66.68 % 82.23 % 59.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
24 TANet code 66.37 % 81.15 % 60.10 % 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.
25 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
65.85 % 81.05 % 59.17 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
26 SAANet 65.52 % 82.29 % 58.81 % 0.10 s 1 core @ 2.5 Ghz (Python)
27 Pointpillar_TV 65.12 % 78.88 % 58.73 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
28 FCY
This method makes use of Velodyne laser scans.
64.64 % 80.76 % 58.05 % 0.02 s GPU @ 2.5 Ghz (Python)
29 Sogo_MM 63.50 % 71.57 % 55.24 % 1.5 s GPU @ 2.5 Ghz (C/C++)
30 SubCNN 63.36 % 71.97 % 55.42 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
31 deprecated 63.08 % 83.73 % 53.51 % 0.05 s GPU @ 2.0 Ghz (Python)
32 CentrNet-v1
This method makes use of Velodyne laser scans.
62.11 % 78.10 % 55.54 % 0.03 s GPU @ 2.5 Ghz (Python)
33 Pose-RCNN 62.02 % 75.74 % 53.99 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
34 SCNet
This method makes use of Velodyne laser scans.
61.11 % 77.77 % 54.82 % 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.
35 PP-3D 60.09 % 76.73 % 53.41 % 0.1 s 1 core @ 2.5 Ghz (Python)
36 AVOD-FPN
This method makes use of Velodyne laser scans.
code 58.70 % 69.21 % 53.47 % 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.
37 DDB
This method makes use of Velodyne laser scans.
58.65 % 75.36 % 52.85 % 0.05 s GPU @ 2.5 Ghz (Python)
38 Deep3DBox 58.56 % 68.31 % 50.30 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
39 3DOP
This method uses stereo information.
code 58.45 % 72.24 % 51.91 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
40 Complexer-YOLO
This method makes use of Velodyne laser scans.
58.28 % 65.41 % 54.27 % 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.
41 DeepStereoOP 56.55 % 69.36 % 49.37 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
42 Mono3D code 53.96 % 67.33 % 47.91 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
43 AVOD
This method makes use of Velodyne laser scans.
code 51.05 % 64.81 % 45.12 % 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.
44 BirdNet+
This method makes use of Velodyne laser scans.
code 50.94 % 69.92 % 47.01 % 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. arXiv:2003.04188 [cs.CV] 2020.
45 Mono3CN 50.58 % 66.58 % 45.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
46 FRCNN+Or code 49.53 % 63.45 % 43.65 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
47 MonoPSR code 49.32 % 58.63 % 43.05 % 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.
48 BirdNet
This method makes use of Velodyne laser scans.
45.03 % 62.69 % 41.88 % 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.
49 SparsePool code 43.50 % 59.77 % 39.36 % 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.
50 UDI-mono3D 42.21 % 57.16 % 36.30 % 0.05 s 1 core @ 2.5 Ghz (Python)
51 NL_M3D 41.19 % 57.44 % 36.24 % 0.2 s 1 core @ 2.5 Ghz (Python)
52 sensekitti code 41.14 % 47.48 % 35.07 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
53 CG-Stereo
This method uses stereo information.
40.64 % 60.24 % 35.55 % 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.
54 MonoPair 39.47 % 53.36 % 33.95 % 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.
55 MTMono3d 39.06 % 55.32 % 31.70 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
56 HG-Mono 38.48 % 54.04 % 32.01 % 0.46 s GPU @ 2.5 Ghz (C/C++)
57 SS3D_HW 37.68 % 52.40 % 32.33 % 0.4 s GPU @ 2.5 Ghz (Python)
58 Disp R-CNN (velo)
This method uses stereo information.
code 35.77 % 50.66 % 30.96 % 0.42 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.
59 Disp R-CNN
This method uses stereo information.
code 35.76 % 50.64 % 30.95 % 0.42 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.
60 Shift R-CNN (mono) code 34.77 % 51.95 % 31.10 % 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.
61 SparsePool code 34.56 % 43.33 % 31.09 % 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.
62 KNN-GCNN 34.03 % 39.32 % 31.17 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
63 HWFD 32.51 % 35.23 % 28.94 % 0.21 s one 1080Ti
64 Point-GNN
This method makes use of Velodyne laser scans.
code 32.37 % 36.29 % 29.81 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
65 dgist_multiDetNet 31.84 % 36.92 % 28.02 % 0.08 s GPU Titanx Pascal (Python)
66 D4LCN code 31.70 % 48.03 % 26.99 % 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.
67 Center3D 31.68 % 51.51 % 28.45 % 0.05 s GPU @ 3.5 Ghz (Python)
68 Faster RCNN + Gr + A 31.55 % 36.35 % 28.43 % 1.29 s GPU @ 2.5 Ghz (Python)
69 M3D-RPN code 31.09 % 48.11 % 26.10 % 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 .
70 Faster RCNN + A 30.81 % 36.25 % 27.51 % 0.19 s GPU @ 2.5 Ghz (Python)
71 Faster RCNN + G 30.61 % 36.19 % 27.22 % 1.1 s GPU @ 2.5 Ghz (Python)
72 DGIST-CellBox 30.34 % 35.69 % 27.10 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
73 Faster RCNN + A 30.12 % 36.03 % 26.98 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
74 HR-faster-rcnn 29.82 % 36.89 % 26.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
75 bin 29.63 % 35.40 % 25.98 % 15ms s GPU @ >3.5 Ghz (Python)
76 DP3D 28.41 % 42.17 % 24.02 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
77 AtrousDet 28.26 % 34.10 % 24.69 % 0.05 s TITAN X
78 SS3D 27.79 % 42.95 % 24.26 % 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.
79 DP3D 27.47 % 40.80 % 24.16 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
80 ReSqueeze 27.40 % 36.26 % 24.04 % 0.03 s GPU @ >3.5 Ghz (Python)
81 cascadercnn 26.59 % 33.81 % 23.48 % 0.36 s 4 cores @ 2.5 Ghz (Python)
82 merge12-12 26.39 % 33.49 % 22.83 % 0.2 s 4 cores @ 2.5 Ghz (Python)
83 PG-MonoNet 26.37 % 35.44 % 23.38 % 0.19 s GPU @ 2.5 Ghz (Python)
84 cas+res+soft 26.32 % 33.63 % 22.75 % 0.2 s 4 cores @ 2.5 Ghz (Python)
85 GA2500 26.08 % 32.91 % 22.06 % 0.2 s 1 core @ 2.5 Ghz (Python)
86 GA_rpn500 26.08 % 32.91 % 22.06 % 1 s 1 core @ 2.5 Ghz (Python)
87 DAM 26.05 % 34.25 % 22.30 % 1 s GPU @ 2.5 Ghz (Python)
88 GA_FULLDATA 25.80 % 33.35 % 22.70 % 1 s 4 cores @ 2.5 Ghz (Python)
89 CSFADet 25.77 % 32.19 % 22.78 % 0.05 s GPU @ 2.5 Ghz (Python)
90 GA_BALANCE 25.27 % 33.79 % 22.03 % 1 s 1 core @ 2.5 Ghz (Python)
91 cas_retina 25.24 % 31.74 % 22.30 % 0.2 s 4 cores @ 2.5 Ghz (Python)
92 cas_retina_1_13 25.01 % 31.17 % 22.12 % 0.03 s 4 cores @ 2.5 Ghz (Python)
93 bigger_ga 24.64 % 31.31 % 21.06 % 1 s 1 core @ 2.5 Ghz (Python)
94 PB3D
This method uses stereo information.
23.93 % 38.00 % 21.58 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
95 CRCNNA 23.88 % 29.91 % 20.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
96 FD2 23.83 % 35.75 % 20.79 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
97 GAFM 22.84 % 31.62 % 19.88 % 0.5 s 1 core @ 2.5 Ghz (Python)
98 JSU-NET 22.83 % 31.58 % 19.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
99 yolo4 22.04 % 30.58 % 19.33 % 0.02 s 1 core @ 2.5 Ghz (Python)
100 ga50 21.59 % 29.77 % 18.77 % 1 s 1 core @ 2.5 Ghz (Python)
101 fasterrcnn 21.52 % 28.50 % 18.86 % 0.2 s 4 cores @ 2.5 Ghz (Python)
102 ZKNet 21.51 % 28.26 % 18.83 % 0.01 s GPU @ 2.0 Ghz (Python)
103 yolo4_5l 21.48 % 29.16 % 19.07 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
1