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 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.
2 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.
3 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.
4 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.
5 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.
6 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.
7 D3D 94.66 % 95.43 % 89.72 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
8 CN 94.60 % 97.86 % 89.81 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
9 BM-NET 94.49 % 95.09 % 85.06 % 0.5 s GPU @ 2.5 Ghz (Python + C/C++)
10 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.
11 EPNet 94.44 % 96.15 % 89.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
12 CPRCCNN 94.42 % 96.33 % 89.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
13 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.
14 OAP 93.93 % 96.85 % 86.37 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
15 DGIST-CellBox 93.90 % 95.86 % 88.26 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
16 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.
17 Noah CV Lab - SSL 93.65 % 94.02 % 86.02 % 0.1 s GPU @ 2.5 Ghz (Python)
18 Associate-3Ddet_v2 93.62 % 96.81 % 88.46 % 0.04 s 1 core @ 2.5 Ghz (Python)
19 MVX-Net++ 93.58 % 96.41 % 88.51 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
20 CLOCs_PointCas 93.55 % 96.69 % 86.16 % 0.1 s GPU @ 2.5 Ghz (Python)
21 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.
22 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.
23 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.
24 AIMC-RUC 93.47 % 96.75 % 88.35 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
25 FichaDL 93.46 % 96.00 % 84.39 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
26 AAMF-SSD 93.45 % 96.72 % 88.31 % 0.05 s GPU @ 2.5 Ghz (Python)
27 RGB-SSD 93.44 % 96.72 % 88.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 PC-RGNN 93.43 % 96.81 % 88.25 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
29 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.
30 KNN-GCNN 93.39 % 96.19 % 88.17 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
31 F-3DNet 93.38 % 96.51 % 88.32 % 0.5 s GPU @ 2.5 Ghz (Python)
32 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. arXiv preprint arXiv:2004.12636 2020.
33 FLID 93.35 % 95.90 % 85.69 % 0.04 s GPU @ 2.5 Ghz (Python)
34 dgist_multiDetNet 93.28 % 94.80 % 86.14 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
35 CFENet 93.26 % 93.91 % 86.99 % 4 s GPU @ 2.5 Ghz (Python + C/C++)
36 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.
37 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.
38 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.
39 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.
40 ELE 93.14 % 98.44 % 90.32 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
41 RethinkDet3D 93.14 % 96.16 % 88.17 % 0.15 s 1 core @ 2.5 Ghz (Python)
42 Discrete-PointDet 93.14 % 96.36 % 87.82 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
43 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.
44 PVF-NET 93.08 % 96.03 % 88.04 % 0.1 s 1 core @ 2.5 Ghz (Python)
45 RGB3D
This method makes use of Velodyne laser scans.
93.07 % 96.54 % 88.04 % 0.39 s GPU @ 2.5 Ghz (Python)
46 SerialR-FCN+SG-NMS 93.03 % 95.81 % 83.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
47 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++)
48 CLOCs_SecCas 92.95 % 95.43 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
49 cvMax 92.84 % 96.14 % 87.87 % 0.04 s GPU @ >3.5 Ghz (Python)
50 OHS 92.81 % 96.21 % 89.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
51 deprecated 92.79 % 96.12 % 87.78 % 0.04 s GPU @ 2.5 Ghz (Python)
52 PointCSE 92.78 % 95.99 % 87.66 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
53 IGRP 92.78 % 96.28 % 87.81 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
54 Mono3CN 92.76 % 95.51 % 84.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 MuRF 92.74 % 95.74 % 87.64 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
56 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.
57 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.
58 Chovy 92.69 % 96.06 % 89.74 % 0.04 s GPU @ 2.5 Ghz (Python)
59 PPFNet code 92.68 % 96.32 % 87.66 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
60 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.
61 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.
62 92.65 % 96.09 % 89.72 %
63 deprecated 92.60 % 96.20 % 89.60 % - -
64 deprecated 92.59 % 96.21 % 89.58 % 0.05 s GPU @ >3.5 Ghz (Python)
65 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.
66 SPA 92.56 % 95.96 % 87.60 % 0.1 s 1 core @ 2.5 Ghz (Python)
67 DEFT 92.55 % 96.17 % 89.51 % 1 s GPU @ 2.5 Ghz (Python)
68 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.
69 PPBA 92.46 % 95.22 % 87.53 % NA s GPU @ 2.5 Ghz (Python)
70 TBU 92.46 % 95.22 % 87.53 % NA s GPU @ 2.5 Ghz (Python)
71 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. CVPR 2020.
72 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++)
73 92.39 % 95.84 % 89.51 %
74 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.
75 OneCoLab SicNet 92.32 % 95.57 % 89.71 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
76 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.
77 LZY_RCNN 92.16 % 93.40 % 89.60 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
78 yolo4 92.13 % 94.20 % 79.89 % 0.02 s 1 core @ 2.5 Ghz (Python)
79 TBD 92.12 % 93.48 % 89.56 % 0.05 s GPU @ 2.5 Ghz (Python)
80 IE-PointRCNN 92.08 % 96.01 % 87.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 PBASN code 92.07 % 95.51 % 87.04 % NA s GPU @ 2.5 Ghz (Python)
82 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.
83 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.
84 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.
85 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.
86 MBR-SSD 91.83 % 93.46 % 84.97 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
87 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.
88 deprecated 91.76 % 96.53 % 83.90 % 0.05 s 1 core @ 2.5 Ghz (Python)
89 3DBN_2 91.75 % 95.34 % 89.12 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
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90 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.
91 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.
92 PiP 91.67 % 94.35 % 88.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
93 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.
94 Faster RCNN + A 91.60 % 94.77 % 81.43 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
95 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.
96 deprecated 91.59 % 94.34 % 79.14 % 0.05 s GPU @ 2.0 Ghz (Python)
97 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.
98 RUC 91.40 % 95.02 % 88.41 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
99 CU-PointRCNN 91.34 % 97.25 % 86.98 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
100 deprecated 91.31 % 96.90 % 83.91 % 0.06 s GPU @ >3.5 Ghz (Python)
101 Faster RCNN + G 91.28 % 94.34 % 81.02 % 1.1 s GPU @ 2.5 Ghz (Python)
102 Faster RCNN + Gr + A 91.25 % 94.09 % 81.25 % 1.29 s GPU @ 2.5 Ghz (Python)
103 OACV 91.21 % 94.23 % 83.07 % 0.23 s GPU @ 2.5 Ghz (Python)
104 CentrNet-v1
This method makes use of Velodyne laser scans.
91.21 % 94.22 % 88.36 % 0.03 s GPU @ 2.5 Ghz (Python)
105 CentrNet-FG 91.21 % 94.05 % 88.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
106 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.
107 Faster RCNN + A 91.19 % 94.43 % 80.99 % 0.19 s GPU @ 2.5 Ghz (Python)
108 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.
109 autonet 91.17 % 93.70 % 88.10 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
110 WS3D
This method makes use of Velodyne laser scans.
91.15 % 95.13 % 86.52 % 0.1 s GPU @ 2.5 Ghz (Python)
111 PointPiallars_SECA 91.12 % 93.66 % 87.94 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
112 DDB
This method makes use of Velodyne laser scans.
91.12 % 93.71 % 87.34 % 0.05 s GPU @ 2.5 Ghz (Python)
113 EPENet 91.11 % 94.31 % 88.02 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
114 anonymous 91.08 % 96.57 % 82.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
115 SSL-RTM3D 91.07 % 96.44 % 81.19 % 0.03 s 1 core @ 2.5 Ghz (Python)
116 FII-CenterNet 91.03 % 94.48 % 83.00 % 0.09 s GPU @ 2.5 Ghz (Python)
117 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.
118 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.
119 Bit 90.96 % 93.84 % 87.47 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
120 JSU-NET 90.90 % 96.41 % 80.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
121 GAFM 90.90 % 96.46 % 80.70 % 0.5 s 1 core @ 2.5 Ghz (Python)
122 PatchNet 90.87 % 93.82 % 79.62 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
123 GA_BALANCE 90.86 % 96.19 % 78.40 % 1 s 1 core @ 2.5 Ghz (Python)
124 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.
125 MVSLN 90.81 % 96.12 % 83.39 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
126 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.
127 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.
128 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++)
129 GA_FULLDATA 90.73 % 96.31 % 78.22 % 1 s 4 cores @ 2.5 Ghz (Python)
130 Simple3D Net 90.70 % 93.54 % 87.81 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
131 HR-SECOND code 90.68 % 93.72 % 85.63 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
132 GA2500 90.68 % 95.86 % 80.29 % 0.2 s 1 core @ 2.5 Ghz (Python)
133 GA_rpn500 90.68 % 95.86 % 80.29 % 1 s 1 core @ 2.5 Ghz (Python)
134 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.
135 SFB-SECOND 90.67 % 96.17 % 85.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
136 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++)
137 PTS
This method makes use of Velodyne laser scans.
code 90.64 % 95.74 % 85.41 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
138 yolo4 90.63 % 94.71 % 80.38 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
139 baseline 90.59 % 93.29 % 87.18 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
140 VOXEL_FPN_HR 90.55 % 93.76 % 85.42 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
141 FOFNet
This method makes use of Velodyne laser scans.
90.52 % 94.00 % 85.20 % 0.04 s GPU @ 2.5 Ghz (Python)
142 MP 90.50 % 93.86 % 85.17 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
143 Sogo_MM 90.46 % 94.31 % 80.62 % 1.5 s GPU @ 2.5 Ghz (C/C++)
144 bigger_ga 90.38 % 95.76 % 77.92 % 1 s 1 core @ 2.5 Ghz (Python)
145 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. arXiv preprint arXiv:2003.05505 2020.
146 AtrousDet 90.35 % 95.94 % 77.94 % 0.05 s TITAN X
147 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.
148 RUC code 90.24 % 92.60 % 86.55 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
149 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.
150 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.
151 BVVF 90.15 % 95.65 % 84.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
152 FCY
This method makes use of Velodyne laser scans.
90.15 % 93.27 % 86.60 % 0.02 s GPU @ 2.5 Ghz (Python)
153 SAANet 90.14 % 95.93 % 82.95 % 0.10 s 1 core @ 2.5 Ghz (Python)
154 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.
155 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.
156 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.
157 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.
158 RUC code 89.93 % 93.12 % 85.44 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
159 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.
160 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.
161 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.
162 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.
163 4D-MSCNN+CRL
This method uses stereo information.
89.37 % 92.40 % 77.00 % 0.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
164 R-FCN(FPN) 89.35 % 93.53 % 79.35 % 0.2 s 1 core @ 2.5 Ghz (Python)
165 cas+res+soft 89.14 % 94.54 % 78.37 % 0.2 s 4 cores @ 2.5 Ghz (Python)
166 merge12-12 88.96 % 94.58 % 78.22 % 0.2 s 4 cores @ 2.5 Ghz (Python)
167 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.
168 autoRUC 88.88 % 94.23 % 81.35 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
169 Prune 88.85 % 94.20 % 81.31 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
170 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.
171 SS3D_HW 88.68 % 94.49 % 68.79 % 0.4 s GPU @ 2.5 Ghz (Python)
172 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.
173 Multi-task DG 88.65 % 93.83 % 76.16 % 0.06 s GPU @ 2.5 Ghz (Python)
174 CRCNNA 88.59 % 94.82 % 76.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
175 3DNN 88.56 % 94.52 % 81.51 % 0.09 s GPU @ 2.5 Ghz (Python)
176 CSFADet 88.54 % 93.75 % 78.62 % 0.05 s GPU @ 2.5 Ghz (Python)
177 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.
178 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.
179 PSMD 88.47 % 93.67 % 75.62 % 0.1 s GPU @ 2.5 Ghz (Python)
180 RCD 88.46 % 92.52 % 83.73 % 0.1 s GPU @ 2.5 Ghz (Python)
181 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.
182 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++)
183 PP-3D 88.35 % 93.71 % 80.84 % 0.1 s 1 core @ 2.5 Ghz (Python)
184 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.
185 anonymous 88.16 % 96.22 % 75.72 % 1 s 1 core @ 2.5 Ghz (C/C++)
186 Multi-3D
This method makes use of Velodyne laser scans.
87.87 % 93.70 % 76.07 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
187 ga50 87.65 % 95.76 % 75.14 % 1 s 1 core @ 2.5 Ghz (Python)
188 cas_retina 87.64 % 93.87 % 75.30 % 0.2 s 4 cores @ 2.5 Ghz (Python)
189 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.
190 MonoSS 87.46 % 93.15 % 77.58 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
191 cascadercnn 87.36 % 89.37 % 73.42 % 0.36 s 4 cores @ 2.5 Ghz (Python)
192 CDN
This method uses stereo information.
87.19 % 95.85 % 79.43 % 0.6 s GPU @ 2.5 Ghz (Python)
193 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.
194 NL_M3D 86.80 % 91.31 % 72.37 % 0.2 s 1 core @ 2.5 Ghz (Python)
195 voxelrcnn 86.69 % 94.60 % 79.91 % 15 s 1 core @ 2.5 Ghz (C/C++)
196 anm 86.52 % 94.88 % 76.46 % 3 s 1 core @ 2.5 Ghz (C/C++)
197 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.
198 ReSqueeze 86.12 % 90.35 % 76.53 % 0.03 s GPU @ >3.5 Ghz (Python)
199 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.
200 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.
201 ResNet-RRC w/RGBD 85.58 % 91.32 % 74.80 % 0.057 s GPU @ 1.5 Ghz (Python + C/C++)
202 cas_retina_1_13 85.48 % 91.54 % 74.60 % 0.03 s 4 cores @ 2.5 Ghz (Python)
203 ResNet-RRC 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.
204 Cmerge 85.32 % 93.40 % 70.57 % 0.2 s 4 cores @ 2.5 Ghz (Python)
205 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.
206 RAR-Net 85.08 % 89.04 % 69.26 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
207 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 .
208 CDN-PL++
This method uses stereo information.
85.01 % 94.66 % 77.60 % 0.4 s GPU @ 2.5 Ghz (C/C++)
209 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.
210 bifpn_fsrn 84.93 % 93.68 % 74.45 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
211 IDA-3D
This method uses stereo information.
84.92 % 92.79 % 74.75 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
212 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.
213 LPN 84.77 % 89.19 % 74.08 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
214 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.
215 SECA 84.60 % 92.51 % 79.53 % 1 s GPU @ 2.5 Ghz (Python)
216 PG-MonoNet 84.42 % 88.61 % 68.59 % 0.19 s GPU @ 2.5 Ghz (Python)
217 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.
218 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.
219 HG-Mono 84.01 % 89.65 % 65.28 % 0.46 s GPU @ 2.5 Ghz (C/C++)
220 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.
221 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.
222 seivl 83.60 % 90.35 % 81.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
223 ASOD 83.52 % 94.09 % 68.68 % 0.28 s GPU @ 2.5 Ghz (Python)
224 softretina 83.30 % 93.55 % 70.59 % 0.16 s 4 cores @ 2.5 Ghz (Python)
225 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.
226 SSL-RTM3D Res18 82.97 % 93.35 % 73.11 % 0.02 s GPU @ 2.5 Ghz (Python)
227 ZKNet 82.96 % 92.17 % 72.43 % 0.01 s GPU @ 2.0 Ghz (Python)
228 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.
229 DP3D 82.81 % 87.85 % 66.80 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
230 Retinanet100 82.73 % 93.97 % 68.37 % 0.2 s 4 cores @ 2.5 Ghz (Python)
231 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.
232 DP3D 82.63 % 87.90 % 66.62 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
233 Pseudo-LiDAR E2E
This method uses stereo information.
82.54 % 94.00 % 75.31 % 0.4 s GPU @ 2.5 Ghz (Python)
234 Disp R-CNN
This method uses stereo information.
code 82.47 % 93.15 % 70.35 % 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.
235 Disp R-CNN (velo)
This method uses stereo information.
code 82.40 % 93.11 % 70.26 % 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.
236 cascade_gw 82.35 % 85.98 % 71.60 % 0.2 s 4 cores @ 2.5 Ghz (Python)
237 deprecated 82.23 % 92.21 % 67.87 % 1 core @ 2.5 Ghz (C/C++)
238 S3D 82.18 % 91.77 % 67.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
239 Stereo3D
This method uses stereo information.
82.15 % 94.81 % 62.17 % 0.1 s GPU 1080Ti
240 LNET 82.02 % 91.49 % 67.71 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
241 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.
242 CBNet 81.70 % 91.47 % 72.02 % 1 s 4 cores @ 2.5 Ghz (Python)
243 Resnet101Faster rcnn 81.44 % 91.08 % 71.52 % 1 s 1 core @ 2.5 Ghz (Python)
244 yyyyolo 81.33 % 94.36 % 68.72 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
245 LCD3D 81.25 % 91.29 % 64.55 % 0.03 s GPU @ 2.5 Ghz (Python)
246 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.
247 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.
248 MTDP 80.97 % 89.03 % 66.91 % 0.15 s GPU @ 2.0 Ghz (Python)
249 RFCN_RFB 80.89 % 88.07 % 69.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
250 centernet 80.78 % 90.29 % 70.53 % 0.01 s GPU @ 2.5 Ghz (Python)
251 UM3D_TUM 80.36 % 92.88 % 65.95 % 0.05 s 1 core @ 2.5 Ghz (Python)
252 3D-GCK 80.19 % 89.55 % 68.08 % 24 ms Tesla V100
253 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)
254 YoloMono3D 79.63 % 92.37 % 59.69 % 0.05 s GPU @ 2.5 Ghz (Python)
255 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)
256 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)
257 DA-3Ddet 79.47 % 89.49 % 63.04 % 0.4 s GPU @ 2.5 Ghz (Python)
258 SceneNet 79.26 % 90.70 % 67.98 % 0.03 s GPU @ 2.5 Ghz (C/C++)
259 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.
260 MTNAS 78.82 % 88.96 % 67.07 % 0.02 s 1 core @ 2.5 Ghz (python)
261 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.
262 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.
263 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.
264 yolov3_warp 77.61 % 92.24 % 65.70 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
265 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.
266 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.
267 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.
268 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.
269 FailNet-Fusion
This method makes use of Velodyne laser scans.
76.90 % 74.55 % 71.94 % 0.1 s 1 core @ 2.5 Ghz (Python)
270 avodC 76.58 % 87.30 % 71.65 % 0.1 s GPU @ 2.5 Ghz (Python)
271 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.
272 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.
273 FailNet-LIDAR
This method makes use of Velodyne laser scans.
76.26 % 74.16 % 71.24 % 0.1 s 1 core @ 2.5 Ghz (Python)
274 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.
275 bin 76.16 % 78.73 % 63.39 % 15ms s GPU @ >3.5 Ghz (Python)
276 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.
277 VoxelNet(Unofficial) 75.22 % 81.37 % 68.74 % 0.5 s GPU @ 2.0 Ghz (Python)
278 RFCN 75.14 % 83.04 % 61.55 % 0.2 s 4 cores @ 2.5 Ghz (Python)
279 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.
280 OC Stereo
This method uses stereo information.
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.
281 yolo800 74.31 % 78.93 % 63.83 % 0.13 s 4 cores @ 2.5 Ghz (Python)
282 3DVSSD 74.11 % 86.99 % 63.57 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
283 FD2 73.93 % 88.65 % 64.62 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
284 BdCost+DA+BB+MS 73.72 % 85.18 % 57.79 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
285 m-prcnn
This method uses stereo information.
73.64 % 87.64 % 57.03 % 0.43 s 1 core @ 2.5 Ghz (Python)
286 BdCost+DA+MS 73.62 % 85.03 % 58.94 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
287 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.
288 stereo_sa
This method uses stereo information.
72.99 % 87.88 % 63.49 % 0.3 s GPU @ 2.5 Ghz (Python)
289 RuiRUC 72.08 % 87.48 % 55.28 % 0.12 s 1 core @ 2.5 Ghz (Python)
290 ANM 71.97 % 87.17 % 55.19 % 0.12 s 1 core @ 2.5 Ghz (Python)
291 RFBnet 71.66 % 87.25 % 63.00 % 0.2 s 4 cores @ 2.5 Ghz (Python)
292 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.
293 GPVL 71.06 % 81.67 % 54.96 % 10 s 1 core @ 2.5 Ghz (C/C++)
294 BdCost+DA+BB 70.86 % 85.52 % 56.19 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
295 DAM 70.78 % 90.08 % 61.38 % 1 s GPU @ 2.5 Ghz (Python)
296 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.
297 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.
298 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.
299 fasterrcnn 69.45 % 74.76 % 60.20 % 0.2 s 4 cores @ 2.5 Ghz (Python)
300 Decoupled-3D v2 68.17 % 88.64 % 54.74 % 0.08 s GPU @ 2.5 Ghz (C/C++)
301 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.
302 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.
303 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.
304 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.
305 Fast-SSD 66.79 % 85.19 % 57.89 % 0.06 s GTX650Ti
306 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.
307 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.
308 E-VoxelNet 65.33 % 68.00 % 57.84 % 0.1 s GPU @ 2.5 Ghz (Python)
309 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.
310 BdCost48-25C 64.63 % 81.42 % 52.22 % 4 s 1 core @ 2.5 Ghz (C/C++)
311 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.
312 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.
313 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.
314 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.
315 yl_net 61.78 % 66.00 % 60.36 % 0.03 s GPU @ 2.5 Ghz (Python)
316 Lidar_ROI+Yolo(UJS) 61.71 % 73.32 % 53.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
317 GNN 61.48 % 79.09 % 51.06 % 0.2 s 1 core @ 2.5 Ghz (Python)
318 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.
319 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.
320 SDP-Net-s 59.94 % 65.51 % 57.20 % 12ms GPU @ 2.5 Ghz (Python)
321 RADNet-Mono 59.85 % 67.47 % 54.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
322 monoref3d 58.97 % 78.11 % 47.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
323 ref3D 58.97 % 78.11 % 47.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
324 100Frcnn 58.92 % 82.09 % 49.04 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
325 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.
326 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.
327 ref3D 57.16 % 77.96 % 45.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
328 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.
329 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.
330 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.
331 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). .
332 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.
333 RT3D-GMP
This method uses stereo information.
51.95 % 62.41 % 39.14 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
334 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 .
335 FailNet-Mono 47.95 % 59.59 % 41.33 % 0.1 s 1 core @ 2.5 Ghz (Python)
336 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.
337 softyolo 45.97 % 66.08 % 38.02 % 0.16 s 4 cores @ 2.5 Ghz (Python)
338 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.
339 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.
340 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.
341 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.
342 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.
343 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.
344 Licar
This method makes use of Velodyne laser scans.
35.19 % 42.34 % 33.97 % 0.09 s GPU @ 2.0 Ghz (Python)
345 KD53-20 34.76 % 51.76 % 29.39 % 0.19 s 4 cores @ 2.5 Ghz (Python)
346 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)
347 FCN-Depth code 25.05 % 52.32 % 18.07 % 1 s GPU @ 1.5 Ghz (Matlab + C/C++)
348 Scan_YOLO 21.93 % 24.97 % 20.07 % 0.1 s 4 cores @ 3.0 Ghz (Python)
349 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.
350 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.
351 R-CNN_VGG 21.36 % 29.38 % 16.61 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
352 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.
353 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.
354 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.
355 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.
356 FCPP 0.07 % 0.01 % 0.07 % 0.02 s 1 core @ 2.0 Ghz (Python + C/C++)
357 ANM 0.01 % 0.01 % 0.02 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
358 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.
359 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.
360 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 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.
7 dgist_multiDetNet 79.50 % 88.33 % 75.38 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
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 Multi-3D
This method makes use of Velodyne laser scans.
75.32 % 86.00 % 69.55 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
18 Faster RCNN + Gr + A 74.95 % 86.95 % 69.50 % 1.29 s GPU @ 2.5 Ghz (Python)
19 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.
20 Multi-task DG 73.99 % 85.21 % 68.06 % 0.06 s GPU @ 2.5 Ghz (Python)
21 Faster RCNN + G 73.75 % 85.51 % 68.54 % 1.1 s GPU @ 2.5 Ghz (Python)
22 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.
23 Sogo_MM 72.82 % 84.99 % 67.42 % 1.5 s GPU @ 2.5 Ghz (C/C++)
24 Faster RCNN + A 72.67 % 86.21 % 67.55 % 0.19 s GPU @ 2.5 Ghz (Python)
25 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.
26 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.
27 FRCNN-WS 72.26 % 84.20 % 67.47 % 0.22 s 1 core @ 3.0 Ghz (Python)
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 OHS 62.31 % 71.43 % 59.24 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
52 cas_retina_1_13 61.87 % 79.09 % 56.70 % 0.03 s 4 cores @ 2.5 Ghz (Python)
53 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.
54 ReSqueeze 61.33 % 73.69 % 56.65 % 0.03 s GPU @ >3.5 Ghz (Python)
55 RPN+BF code 61.22 % 77.06 % 55.22 % 0.6 s GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
56 JSU-NET 61.19 % 83.17 % 56.20 % 0.1 s 1 core @ 2.5 Ghz (Python)
57 RethinkDet3D 60.88 % 70.56 % 56.69 % 0.15 s 1 core @ 2.5 Ghz (Python)
58 Regionlets 60.83 % 73.79 % 54.72 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
59 bin 60.73 % 71.43 % 55.78 % 15ms s GPU @ >3.5 Ghz (Python)
60 60.63 % 69.37 % 57.64 %
61 3DSSD code 60.51 % 72.33 % 56.28 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
62 anm 60.35 % 76.02 % 55.46 % 3 s 1 core @ 2.5 Ghz (C/C++)
63 MVX-Net++ 60.21 % 69.70 % 56.07 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
64 PiP 59.94 % 70.52 % 56.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
65 cascadercnn 59.50 % 78.79 % 54.44 % 0.36 s 4 cores @ 2.5 Ghz (Python)
66 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.
67 58.70 % 68.18 % 54.68 %
68 DDB
This method makes use of Velodyne laser scans.
58.53 % 69.03 % 55.90 % 0.05 s GPU @ 2.5 Ghz (Python)
69 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.
70 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.
71 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.
72 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.
73 PPBA 58.06 % 67.73 % 55.69 % NA s GPU @ 2.5 Ghz (Python)
74 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.
75 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.
76 LPN 57.69 % 71.87 % 53.21 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
77 TBU 57.44 % 67.29 % 54.00 % NA s GPU @ 2.5 Ghz (Python)
78 CentrNet-FG 57.40 % 68.27 % 54.11 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
79 Simple3D Net 57.00 % 66.89 % 54.38 % 0.02 s GPU @ 2.5 Ghz (Python)
80 KNN-GCNN 56.80 % 69.53 % 52.86 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
81 LDAM 56.68 % 64.73 % 54.21 % 24 ms GTX 1080 ti GPU
82 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.
83 yolo800 56.67 % 71.26 % 50.91 % 0.13 s 4 cores @ 2.5 Ghz (Python)
84 ZKNet 56.58 % 71.15 % 51.87 % 0.01 s GPU @ 2.0 Ghz (Python)
85 CentrNet-v1
This method makes use of Velodyne laser scans.
56.57 % 66.27 % 54.19 % 0.03 s GPU @ 2.5 Ghz (Python)
86 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.
87 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++)
88 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.
89 FD2 56.35 % 71.37 % 51.08 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
90 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.
91 RFCN 55.96 % 72.32 % 49.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
92 yolo4 55.78 % 72.49 % 51.11 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
93 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.
94 DAM 55.60 % 74.85 % 50.63 % 1 s GPU @ 2.5 Ghz (Python)
95 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.
96 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.
97 RFCN_RFB 54.98 % 70.61 % 48.75 % 0.2 s 4 cores @ 2.5 Ghz (Python)
98 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.
99 yolo4 54.30 % 73.16 % 49.46 % 0.02 s 1 core @ 2.5 Ghz (Python)
100 CHTTL MMF 54.28 % 72.79 % 49.31 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
101 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.
102 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.
103 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.
104 fasterrcnn 53.42 % 69.29 % 48.76 % 0.2 s 4 cores @ 2.5 Ghz (Python)
105 3DBN_2 53.26 % 63.82 % 50.76 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
106 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.
107 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.
108 deprecated 52.32 % 67.93 % 47.77 % 0.05 s GPU @ 2.0 Ghz (Python)
109 PP-3D 52.11 % 63.07 % 49.79 % 0.1 s 1 core @ 2.5 Ghz (Python)
110 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.
111 MTDP 51.81 % 68.12 % 46.95 % 0.15 s GPU @ 2.0 Ghz (Python)
112 TBD 51.31 % 61.14 % 47.82 % 0.05 s GPU @ 2.5 Ghz (Python)
113 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.
114 centernet 51.09 % 69.27 % 45.40 % 0.01 s GPU @ 2.5 Ghz (Python)
115 FCY
This method makes use of Velodyne laser scans.
50.88 % 59.73 % 48.61 % 0.02 s GPU @ 2.5 Ghz (Python)
116 PPFNet code 50.52 % 57.82 % 47.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
117 FOFNet
This method makes use of Velodyne laser scans.
50.08 % 62.64 % 46.27 % 0.04 s GPU @ 2.5 Ghz (Python)
118 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.
119 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.
120 Resnet101Faster rcnn 49.12 % 64.72 % 44.60 % 1 s 1 core @ 2.5 Ghz (Python)
121 VOXEL_FPN_HR 49.09 % 60.28 % 45.47 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
122 SS3D_HW 49.01 % 64.67 % 42.86 % 0.4 s GPU @ 2.5 Ghz (Python)
123 cascade_gw 48.99 % 67.35 % 44.25 % 0.2 s 4 cores @ 2.5 Ghz (Python)
124 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.
125 MP 48.73 % 60.26 % 45.05 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
126 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.
127 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.
128 PBASN code 46.75 % 54.38 % 44.58 % NA s GPU @ 2.5 Ghz (Python)
129 HR-SECOND code 46.69 % 58.68 % 42.93 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
130 Cmerge 46.51 % 63.68 % 41.60 % 0.2 s 4 cores @ 2.5 Ghz (Python)
131 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.
132 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.
133 NL_M3D 45.03 % 58.46 % 39.22 % 0.2 s 1 core @ 2.5 Ghz (Python)
134 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.
135 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.
136 yyyyolo 44.55 % 60.74 % 39.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
137 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.
138 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.
139 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.
140 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.
141 PG-MonoNet 43.12 % 58.51 % 38.92 % 0.19 s GPU @ 2.5 Ghz (Python)
142 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.
143 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.
144 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. arXiv preprint arXiv:2003.05505 2020.
145 DP3D 42.33 % 57.82 % 38.11 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
146 GNN 42.28 % 58.09 % 37.81 % 0.2 s 1 core @ 2.5 Ghz (Python)
147 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.
148 DP3D 41.71 % 55.28 % 35.73 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
149 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)
150 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.
151 HG-Mono 41.48 % 56.67 % 37.33 % 0.46 s GPU @ 2.5 Ghz (C/C++)
152 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 .
153 Stereo3D
This method uses stereo information.
41.46 % 56.20 % 37.07 % 0.1 s GPU 1080Ti
154 yolov3_warp 40.64 % 55.04 % 36.33 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
155 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.
156 SAANet 40.43 % 51.16 % 38.38 % 0.10 s 1 core @ 2.5 Ghz (Python)
157 Retinanet100 40.03 % 54.30 % 35.33 % 0.2 s 4 cores @ 2.5 Ghz (Python)
158 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.
159 RT3D-GMP
This method uses stereo information.
39.83 % 55.56 % 35.18 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
160 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.
161 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.
162 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.
163 softyolo 39.30 % 54.49 % 36.66 % 0.16 s 4 cores @ 2.5 Ghz (Python)
164 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). .
165 pedestrian_cnn 37.90 % 52.07 % 33.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
166 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.
167 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.
168 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.
169 KD53-20 36.03 % 45.78 % 32.79 % 0.19 s 4 cores @ 2.5 Ghz (Python)
170 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.
171 Lidar_ROI+Yolo(UJS) 35.58 % 47.74 % 31.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
172 34.81 % 44.38 % 32.10 %
173 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.
174 OC Stereo
This method uses stereo information.
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.
175 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.
176 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.
177 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.
178 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.
179 100Frcnn 21.92 % 34.07 % 19.48 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
180 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.
181 R-CNN_VGG 19.97 % 26.62 % 17.96 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
182 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.
183 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.
184 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.
185 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.
186 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.
187 CBNet 1.33 % 1.03 % 1.41 % 1 s 4 cores @ 2.5 Ghz (Python)
188 softretina 0.26 % 0.19 % 0.26 % 0.16 s 4 cores @ 2.5 Ghz (Python)
189 JSyolo 0.12 % 0.19 % 0.12 % 0.16 s 4 cores @ 2.5 Ghz (Python)
190 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.
191 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 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.
2 FichaDL 80.38 % 88.41 % 69.72 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
3 Noah CV Lab - SSL 79.10 % 86.71 % 69.66 % 0.1 s GPU @ 2.5 Ghz (Python)
4 OHS 78.81 % 86.06 % 71.74 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
5 78.42 % 85.79 % 71.80 %
6 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.
7 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.
8 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.
9 TBD 77.34 % 87.15 % 70.53 % 0.05 s GPU @ 2.5 Ghz (Python)
10 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.
11 KNN-GCNN 76.52 % 88.83 % 69.82 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
12 75.98 % 83.71 % 68.80 %
13 Multi-3D
This method makes use of Velodyne laser scans.
75.77 % 84.71 % 65.95 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
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 dgist_multiDetNet 74.74 % 87.22 % 66.15 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
24 PPBA 74.46 % 86.45 % 69.15 % NA s GPU @ 2.5 Ghz (Python)
25 TBU 74.46 % 86.45 % 69.15 % NA s GPU @ 2.5 Ghz (Python)
26 3DBN_2 74.34 % 88.48 % 67.66 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
27 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.
28 Multi-task DG 74.05 % 82.73 % 64.21 % 0.06 s GPU @ 2.5 Ghz (Python)
29 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.
30 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.
31 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.
32 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.
33 FOFNet
This method makes use of Velodyne laser scans.
72.96 % 87.12 % 66.37 % 0.04 s GPU @ 2.5 Ghz (Python)
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 % 0.05 s 1 core @ 2.5 Ghz (Python)
41 Faster RCNN + Gr + A 70.78 % 83.99 % 63.36 % 1.29 s GPU @ 2.5 Ghz (Python)
42 PBASN code 70.21 % 83.96 % 65.10 % NA s GPU @ 2.5 Ghz (Python)
43 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.
44 MP 69.52 % 85.05 % 63.17 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
45 LDAM 69.31 % 80.20 % 63.85 % 24 ms GTX 1080 ti GPU
46 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.
47 DGIST-CellBox 68.92 % 83.72 % 61.32 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
48 CentrNet-FG 68.88 % 83.29 % 61.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
49 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.
50 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.
51 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.
52 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.
53 Faster RCNN + G 68.09 % 83.51 % 60.60 % 1.1 s GPU @ 2.5 Ghz (Python)
54 Faster RCNN + A 67.84 % 82.06 % 60.52 % 0.19 s GPU @ 2.5 Ghz (Python)
55 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++)
56 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.
57 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.
58 Faster RCNN + A 67.15 % 83.77 % 59.85 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
59 SAANet 66.58 % 83.07 % 59.88 % 0.10 s 1 core @ 2.5 Ghz (Python)
60 FII-CenterNet 66.54 % 79.04 % 57.76 % 0.09 s GPU @ 2.5 Ghz (Python)
61 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.
62 FCY
This method makes use of Velodyne laser scans.
65.50 % 81.33 % 59.04 % 0.02 s GPU @ 2.5 Ghz (Python)
63 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.
64 Simple3D Net 64.77 % 79.60 % 58.48 % 0.02 s GPU @ 2.5 Ghz (Python)
65 deprecated 63.34 % 83.91 % 53.78 % 0.05 s GPU @ 2.0 Ghz (Python)
66 Mono3CN 63.29 % 81.46 % 56.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 CentrNet-v1
This method makes use of Velodyne laser scans.
62.99 % 78.90 % 56.46 % 0.03 s GPU @ 2.5 Ghz (Python)
68 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.
69 AtrousDet 62.50 % 79.02 % 53.87 % 0.05 s TITAN X
70 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.
71 DDB
This method makes use of Velodyne laser scans.
61.41 % 78.04 % 55.37 % 0.05 s GPU @ 2.5 Ghz (Python)
72 PP-3D 61.29 % 77.75 % 54.59 % 0.1 s 1 core @ 2.5 Ghz (Python)
73 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.
74 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.
75 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.
76 merge12-12 59.48 % 77.66 % 51.41 % 0.2 s 4 cores @ 2.5 Ghz (Python)
77 cas+res+soft 59.43 % 77.85 % 51.34 % 0.2 s 4 cores @ 2.5 Ghz (Python)
78 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.
79 DAM 58.41 % 76.09 % 49.93 % 1 s GPU @ 2.5 Ghz (Python)
80 cascadercnn 58.08 % 77.24 % 51.13 % 0.36 s 4 cores @ 2.5 Ghz (Python)
81 GA_rpn500 57.82 % 76.06 % 49.00 % 1 s 1 core @ 2.5 Ghz (Python)
82 GA2500 57.82 % 76.06 % 48.99 % 0.2 s 1 core @ 2.5 Ghz (Python)
83 bin 57.62 % 64.36 % 50.70 % 15ms s GPU @ >3.5 Ghz (Python)
84 GA_FULLDATA 57.20 % 75.50 % 50.26 % 1 s 4 cores @ 2.5 Ghz (Python)
85 cas_retina 57.14 % 73.97 % 50.32 % 0.2 s 4 cores @ 2.5 Ghz (Python)
86 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.
87 CSFADet 56.88 % 73.82 % 50.22 % 0.05 s GPU @ 2.5 Ghz (Python)
88 cas_retina_1_13 56.39 % 72.80 % 49.71 % 0.03 s 4 cores @ 2.5 Ghz (Python)
89 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.
90 GA_BALANCE 56.07 % 78.33 % 49.02 % 1 s 1 core @ 2.5 Ghz (Python)
91 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.
92 bigger_ga 55.66 % 73.05 % 47.31 % 1 s 1 core @ 2.5 Ghz (Python)
93 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.
94 ReSqueeze 54.50 % 69.64 % 48.24 % 0.03 s GPU @ >3.5 Ghz (Python)
95 NL_M3D 53.51 % 71.09 % 47.07 % 0.2 s 1 core @ 2.5 Ghz (Python)
96 CRCNNA 53.41 % 69.81 % 46.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
97 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.
98 GAFM 51.40 % 73.43 % 44.61 % 0.5 s 1 core @ 2.5 Ghz (Python)
99 JSU-NET 51.10 % 72.92 % 44.26 % 0.1 s 1 core @ 2.5 Ghz (Python)
100 yolo4 50.62 % 71.71 % 44.18 % 0.02 s 1 core @ 2.5 Ghz (Python)
101 HG-Mono 49.55 % 67.69 % 40.89 % 0.46 s GPU @ 2.5 Ghz (C/C++)
102 ZKNet 49.48 % 66.29 % 42.81 % 0.01 s GPU @ 2.0 Ghz (Python)
103 anm 49.05 % 66.96 % 43.44 % 3 s 1 core @ 2.5 Ghz (C/C++)
104 ga50 49.02 % 70.25 % 42.52 % 1 s 1 core @ 2.5 Ghz (Python)
105 yolo4 48.67 % 67.33 % 43.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
106 LPN 48.57 % 65.77 % 42.66 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
107 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. arXiv preprint arXiv:2003.05505 2020.
108 fasterrcnn 47.87 % 64.39 % 42.03 % 0.2 s 4 cores @ 2.5 Ghz (Python)
109 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.
110 yolo800 47.31 % 63.22 % 42.28 % 0.13 s 4 cores @ 2.5 Ghz (Python)
111 RFCN 46.70 % 62.09 % 40.71 % 0.2 s 4 cores @ 2.5 Ghz (Python)
112 SS3D_HW 45.53 % 61.79 % 39.03 % 0.4 s GPU @ 2.5 Ghz (Python)
113 PG-MonoNet 45.40 % 63.75 % 37.14 % 0.19 s GPU @ 2.5 Ghz (Python)
114 RFCN_RFB 45.28 % 60.06 % 39.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
115 Cmerge 44.87 % 64.38 % 37.80 % 0.2 s 4 cores @ 2.5 Ghz (Python)
116 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.
117 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.
118 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.
119 cascade_gw 42.84 % 63.58 % 36.94 % 0.2 s 4 cores @ 2.5 Ghz (Python)
120 FD2 42.67 % 62.54 % 38.41 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
121 centernet 42.45 % 58.95 % 37.56 % 0.01 s GPU @ 2.5 Ghz (Python)
122 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 .
123 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.
124 MTDP 40.46 % 53.83 % 35.74 % 0.15 s GPU @ 2.0 Ghz (Python)
125 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.
126 GNN 39.80 % 58.30 % 34.56 % 0.2 s 1 core @ 2.5 Ghz (Python)
127 DP3D 37.13 % 53.50 % 32.82 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
128 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.
129 DP3D 36.05 % 52.18 % 30.19 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
130 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.
131 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.
132 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.
133 Retinanet100 32.30 % 46.60 % 28.29 % 0.2 s 4 cores @ 2.5 Ghz (Python)
134 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.
135 yolov3_warp 29.48 % 44.46 % 25.84 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
136 OC Stereo
This method uses stereo information.
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.
137 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)
138 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.
139 softyolo 27.90 % 41.90 % 24.74 % 0.16 s 4 cores @ 2.5 Ghz (Python)
140 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.
141 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.
142 100Frcnn 27.69 % 43.23 % 23.91 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
143 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.
144 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.
145 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.
146 BdCost+DA+BB+MS 25.52 % 33.92 % 21.14 % TBD s 4 cores @ 2.5 Ghz (C/C++)
147 R-CNN_VGG 25.14 % 34.28 % 22.17 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
148 Lidar_ROI+Yolo(UJS) 24.42 % 36.43 % 21.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
149 RT3D-GMP
This method uses stereo information.
22.90 % 33.64 % 19.87 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
150 BdCost+DA+BB 20.00 % 26.87 % 16.76 % TBD s 4 cores @ 2.5 Ghz (C/C++)
151 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.
152 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.
153 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.
154 KD53-20 12.81 % 20.05 % 11.99 % 0.19 s 4 cores @ 2.5 Ghz (Python)
155 yyyyolo 12.52 % 16.29 % 11.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
156 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.
157 Scan_YOLO 4.19 % 5.65 % 4.19 % 0.1 s 4 cores @ 3.0 Ghz (Python)
158 CBNet 0.39 % 0.24 % 0.44 % 1 s 4 cores @ 2.5 Ghz (Python)
159 softretina 0.25 % 0.16 % 0.18 % 0.16 s 4 cores @ 2.5 Ghz (Python)
160 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.
161 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.
162 JSyolo 0.03 % 0.02 % 0.04 % 0.16 s 4 cores @ 2.5 Ghz (Python)
163 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 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.
2 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.
3 CPRCCNN 94.24 % 96.31 % 89.71 % 0.1 s 1 core @ 2.5 Ghz (Python)
4 EPNet 94.22 % 96.13 % 89.68 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
5 D3D 94.18 % 95.22 % 89.14 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
6 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.
7 OAP 93.35 % 96.56 % 85.69 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
8 CLOCs_PointCas 93.34 % 96.66 % 85.87 % 0.1 s GPU @ 2.5 Ghz (Python)
9 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.
10 AIMC-RUC 93.14 % 96.64 % 87.92 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
11 ELE 93.07 % 98.42 % 90.17 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
12 Associate-3Ddet_v2 93.04 % 96.48 % 87.82 % 0.04 s 1 core @ 2.5 Ghz (Python)
13 RGB3D
This method makes use of Velodyne laser scans.
92.94 % 96.52 % 87.83 % 0.39 s GPU @ 2.5 Ghz (Python)
14 MVX-Net++ 92.93 % 96.16 % 87.69 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
15 PC-RGNN 92.91 % 96.54 % 87.67 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
16 AAMF-SSD 92.88 % 96.44 % 87.67 % 0.05 s GPU @ 2.5 Ghz (Python)
17 FLID 92.77 % 95.64 % 85.00 % 0.04 s GPU @ 2.5 Ghz (Python)
18 OHS 92.74 % 96.20 % 89.68 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
19 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++)
20 RGB-SSD 92.73 % 96.38 % 87.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
21 IGRP 92.66 % 96.27 % 87.63 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
22 92.58 % 96.08 % 89.60 %
23 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.
24 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.
25 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.
26 PPFNet code 92.52 % 96.30 % 87.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
27 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.
28 Discrete-PointDet 92.48 % 95.89 % 87.08 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
29 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.
30 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.
31 CLOCs_SecCas 92.37 % 95.16 % 88.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
32 92.32 % 95.83 % 89.39 %
33 OneCoLab SicNet 92.17 % 95.53 % 89.51 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
34 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.
35 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++)
36 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.
37 LZY_RCNN 92.06 % 93.39 % 89.45 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
38 RethinkDet3D 92.04 % 95.68 % 86.97 % 0.15 s 1 core @ 2.5 Ghz (Python)
39 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.
40 TBD 91.97 % 93.46 % 89.36 % 0.05 s GPU @ 2.5 Ghz (Python)
41 PointCSE 91.95 % 95.52 % 86.75 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
42 IE-PointRCNN 91.94 % 96.00 % 86.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
43 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.
44 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.
45 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.
46 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.
47 CU-PointRCNN 91.25 % 97.24 % 86.85 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
48 RUC 91.25 % 95.01 % 88.14 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
49 deprecated 91.18 % 96.19 % 83.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
50 3DBN_2 91.05 % 94.89 % 88.42 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
51 deprecated 91.02 % 94.06 % 78.56 % 0.05 s GPU @ 2.0 Ghz (Python)
52 Mono3CN 90.96 % 94.22 % 82.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 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.
54 SSL-RTM3D 90.70 % 96.34 % 80.72 % 0.03 s 1 core @ 2.5 Ghz (Python)
55 anonymous 90.70 % 96.46 % 82.39 % 1 s 1 core @ 2.5 Ghz (C/C++)
56 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.
57 WS3D
This method makes use of Velodyne laser scans.
90.69 % 94.85 % 85.94 % 0.1 s GPU @ 2.5 Ghz (Python)
58 CentrNet-v1
This method makes use of Velodyne laser scans.
90.48 % 93.79 % 87.43 % 0.03 s GPU @ 2.5 Ghz (Python)
59 DDB
This method makes use of Velodyne laser scans.
90.38 % 93.21 % 86.42 % 0.05 s GPU @ 2.5 Ghz (Python)
60 OACV 90.35 % 93.95 % 81.90 % 0.23 s GPU @ 2.5 Ghz (Python)
61 autonet 90.31 % 93.30 % 87.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
62 MVSLN 90.26 % 95.95 % 82.75 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
63 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.
64 Bit 90.19 % 93.42 % 86.48 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
65 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.
66 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.
67 EPENet 90.09 % 93.83 % 86.76 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
68 FOFNet
This method makes use of Velodyne laser scans.
90.05 % 93.87 % 84.52 % 0.04 s GPU @ 2.5 Ghz (Python)
69 SFB-SECOND 90.04 % 95.99 % 84.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 CentrNet-FG 90.04 % 93.51 % 87.02 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
71 PTS
This method makes use of Velodyne laser scans.
code 90.03 % 95.41 % 84.73 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
72 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. arXiv preprint arXiv:2003.05505 2020.
73 Sogo_MM 89.97 % 94.15 % 79.94 % 1.5 s GPU @ 2.5 Ghz (C/C++)
74 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.
75 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++)
76 PointPiallars_SECA 89.86 % 92.96 % 86.46 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
77 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++)
78 VOXEL_FPN_HR 89.81 % 93.52 % 84.59 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
79 BVVF 89.77 % 95.55 % 84.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
80 baseline 89.69 % 92.61 % 86.03 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
81 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.
82 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.
83 FCY
This method makes use of Velodyne laser scans.
89.49 % 93.02 % 85.72 % 0.02 s GPU @ 2.5 Ghz (Python)
84 SAANet 89.46 % 95.64 % 82.12 % 0.10 s 1 core @ 2.5 Ghz (Python)
85 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.
86 RUC code 89.26 % 92.28 % 85.38 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
87 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.
88 RUC code 88.90 % 92.68 % 84.04 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
89 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.
90 SS3D_HW 88.50 % 94.45 % 68.61 % 0.4 s GPU @ 2.5 Ghz (Python)
91 PSMD 88.29 % 93.59 % 75.35 % 0.1 s GPU @ 2.5 Ghz (Python)
92 Prune 88.10 % 93.86 % 80.41 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
93 autoRUC 88.03 % 93.80 % 80.36 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
94 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++)
95 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.
96 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.
97 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.
98 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.
99 PP-3D 87.46 % 93.09 % 79.88 % 0.1 s 1 core @ 2.5 Ghz (Python)
100 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.
101 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.
102 3DNN 87.08 % 93.78 % 79.72 % 0.09 s GPU @ 2.5 Ghz (Python)
103 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.
104 MonoSS 86.95 % 92.88 % 77.04 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
105 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.
106 CDN
This method uses stereo information.
86.90 % 95.79 % 79.05 % 0.6 s GPU @ 2.5 Ghz (Python)
107 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.
108 voxelrcnn 86.61 % 94.59 % 79.80 % 15 s 1 core @ 2.5 Ghz (C/C++)
109 MBR-SSD 86.57 % 90.97 % 78.03 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
110 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.
111 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.
112 NL_M3D 85.32 % 90.88 % 70.87 % 0.2 s 1 core @ 2.5 Ghz (Python)
113 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.
114 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.
115 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.
116 IDA-3D
This method uses stereo information.
84.32 % 92.63 % 73.98 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
117 CDN-PL++
This method uses stereo information.
84.21 % 94.45 % 76.69 % 0.4 s GPU @ 2.5 Ghz (C/C++)
118 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.
119 SECA 83.99 % 92.34 % 78.85 % 1 s GPU @ 2.5 Ghz (Python)
120 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.
121 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.
122 seivl 83.38 % 90.32 % 81.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 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 .
124 RAR-Net 82.63 % 88.40 % 66.90 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
125 SSL-RTM3D Res18 82.43 % 93.13 % 72.47 % 0.02 s GPU @ 2.5 Ghz (Python)
126 ASOD 82.13 % 93.56 % 67.32 % 0.28 s GPU @ 2.5 Ghz (Python)
127 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.
128 deprecated 81.99 % 92.07 % 67.48 % 1 core @ 2.5 Ghz (C/C++)
129 S3D 81.93 % 91.59 % 67.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
130 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.
131 LNET 81.81 % 91.36 % 67.33 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
132 PG-MonoNet 81.77 % 87.61 % 66.06 % 0.19 s GPU @ 2.5 Ghz (Python)
133 Disp R-CNN
This method uses stereo information.
code 81.61 % 92.91 % 69.20 % 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.
134 Pseudo-LiDAR E2E
This method uses stereo information.
81.56 % 93.74 % 74.23 % 0.4 s GPU @ 2.5 Ghz (Python)
135 HG-Mono 81.53 % 88.76 % 63.12 % 0.46 s GPU @ 2.5 Ghz (C/C++)
136 HR-SECOND code 81.23 % 88.32 % 74.89 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
137 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.
138 DP3D 81.07 % 87.49 % 65.12 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
139 LCD3D 81.01 % 91.20 % 64.29 % 0.03 s GPU @ 2.5 Ghz (Python)
140 Stereo3D
This method uses stereo information.
80.88 % 93.65 % 61.17 % 0.1 s GPU 1080Ti
141 DP3D 80.87 % 87.58 % 64.88 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
142 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.
143 UM3D_TUM 80.15 % 92.80 % 65.77 % 0.05 s 1 core @ 2.5 Ghz (Python)
144 YoloMono3D 78.50 % 91.43 % 58.80 % 0.05 s GPU @ 2.5 Ghz (Python)
145 3D-GCK 78.44 % 88.59 % 66.28 % 24 ms Tesla V100
146 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.
147 DA-3Ddet 77.73 % 89.01 % 61.48 % 0.4 s GPU @ 2.5 Ghz (Python)
148 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.
149 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.
150 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.
151 avodC 75.35 % 86.76 % 70.17 % 0.1 s GPU @ 2.5 Ghz (Python)
152 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.
153 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.
154 OC Stereo
This method uses stereo information.
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.
155 BdCost+DA+BB+MS 72.87 % 84.39 % 57.07 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
156 BdCost+DA+MS 72.65 % 84.06 % 58.08 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
157 BdCost+DA+BB 70.07 % 84.66 % 55.50 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
158 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.
159 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.
160 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.
161 Decoupled-3D v2 67.47 % 88.23 % 54.04 % 0.08 s GPU @ 2.5 Ghz (C/C++)
162 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.
163 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.
164 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.
165 deprecated 65.30 % 69.02 % 63.66 % 0.05 s GPU @ >3.5 Ghz (Python)
166 3DVSSD 65.28 % 79.56 % 55.73 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
167 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.
168 BdCost48-25C 63.90 % 80.69 % 51.54 % 4 s 1 core @ 2.5 Ghz (C/C++)
169 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.
170 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.
171 monoref3d 58.30 % 77.65 % 46.90 % 0.1 s 1 core @ 2.5 Ghz (Python)
172 ref3D 58.30 % 77.65 % 46.90 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
173 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.
174 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.
175 deprecated 57.01 % 62.54 % 54.94 % - -
176 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.
177 ref3D 56.49 % 77.52 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (Python)
178 DEFT 51.66 % 57.41 % 50.02 % 1 s GPU @ 2.5 Ghz (Python)
179 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 .
180 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.
181 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.
182 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.
183 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.
184 ReSqueeze 45.58 % 49.08 % 41.33 % 0.03 s GPU @ >3.5 Ghz (Python)
185 Resnet101Faster rcnn 44.01 % 51.21 % 39.19 % 1 s 1 core @ 2.5 Ghz (Python)
186 anonymous 40.75 % 45.00 % 34.48 % 1 s 1 core @ 2.5 Ghz (C/C++)
187 Chovy 40.34 % 41.64 % 38.31 % 0.04 s GPU @ 2.5 Ghz (Python)
188 cvMax 40.31 % 41.97 % 37.57 % 0.04 s GPU @ >3.5 Ghz (Python)
189 deprecated 40.03 % 40.31 % 37.35 % 0.04 s GPU @ 2.5 Ghz (Python)
190 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. arXiv preprint arXiv:2004.12636 2020.
191 deprecated 38.89 % 40.49 % 35.13 % 0.06 s GPU @ >3.5 Ghz (Python)
192 FD2 38.89 % 48.29 % 34.35 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
193 bin 38.58 % 43.36 % 32.42 % 15ms s GPU @ >3.5 Ghz (Python)
194 PVF-NET 38.53 % 39.57 % 38.23 % 0.1 s 1 core @ 2.5 Ghz (Python)
195 DGIST-CellBox 38.36 % 39.11 % 36.15 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
196 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.
197 dgist_multiDetNet 38.13 % 38.99 % 35.36 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
198 Faster RCNN + A 37.92 % 39.50 % 33.85 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
199 KNN-GCNN 37.80 % 38.80 % 36.52 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
200 JSU-NET 37.60 % 41.33 % 33.41 % 0.1 s 1 core @ 2.5 Ghz (Python)
201 Faster RCNN + G 37.49 % 39.05 % 33.40 % 1.1 s GPU @ 2.5 Ghz (Python)
202 Faster RCNN + A 37.35 % 38.75 % 33.38 % 0.19 s GPU @ 2.5 Ghz (Python)
203 yolo4 37.27 % 38.19 % 32.45 % 0.02 s 1 core @ 2.5 Ghz (Python)
204 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.
205 F-3DNet 37.18 % 38.58 % 36.44 % 0.5 s GPU @ 2.5 Ghz (Python)
206 GAFM 37.08 % 40.28 % 33.08 % 0.5 s 1 core @ 2.5 Ghz (Python)
207 CRCNNA 37.04 % 40.19 % 32.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
208 Faster RCNN + Gr + A 36.95 % 38.22 % 33.16 % 1.29 s GPU @ 2.5 Ghz (Python)
209 CSFADet 36.83 % 39.76 % 32.73 % 0.05 s GPU @ 2.5 Ghz (Python)
210 cas_retina 36.63 % 39.70 % 31.52 % 0.2 s 4 cores @ 2.5 Ghz (Python)
211 GA_BALANCE 36.62 % 38.44 % 31.94 % 1 s 1 core @ 2.5 Ghz (Python)
212 GA_rpn500 36.54 % 38.33 % 32.67 % 1 s 1 core @ 2.5 Ghz (Python)
213 GA2500 36.54 % 38.33 % 32.67 % 0.2 s 1 core @ 2.5 Ghz (Python)
214 cas+res+soft 36.53 % 38.82 % 32.26 % 0.2 s 4 cores @ 2.5 Ghz (Python)
215 merge12-12 36.47 % 38.83 % 32.20 % 0.2 s 4 cores @ 2.5 Ghz (Python)
216 GA_FULLDATA 36.43 % 38.90 % 31.61 % 1 s 4 cores @ 2.5 Ghz (Python)
217 AtrousDet 36.36 % 38.86 % 31.79 % 0.05 s TITAN X
218 bigger_ga 36.21 % 38.41 % 31.58 % 1 s 1 core @ 2.5 Ghz (Python)
219 cas_retina_1_13 35.89 % 39.02 % 31.33 % 0.03 s 4 cores @ 2.5 Ghz (Python)
220 cascadercnn 35.61 % 36.22 % 30.16 % 0.36 s 4 cores @ 2.5 Ghz (Python)
221 Cmerge 35.02 % 38.33 % 29.06 % 0.2 s 4 cores @ 2.5 Ghz (Python)
222 ga50 34.95 % 38.21 % 30.29 % 1 s 1 core @ 2.5 Ghz (Python)
223 softretina 34.57 % 39.31 % 29.27 % 0.16 s 4 cores @ 2.5 Ghz (Python)
224 Retinanet100 34.37 % 39.15 % 28.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
225 ZKNet 34.27 % 38.09 % 29.93 % 0.01 s GPU @ 2.0 Ghz (Python)
226 bifpn_fsrn 33.84 % 37.56 % 29.98 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
227 LPN 33.61 % 34.57 % 29.72 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
228 cascade_gw 33.53 % 34.76 % 29.71 % 0.2 s 4 cores @ 2.5 Ghz (Python)
229 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)
230 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)
231 SceneNet 32.78 % 37.79 % 28.30 % 0.03 s GPU @ 2.5 Ghz (C/C++)
232 MTDP 32.68 % 36.06 % 27.12 % 0.15 s GPU @ 2.0 Ghz (Python)
233 CBNet 32.63 % 36.51 % 29.26 % 1 s 4 cores @ 2.5 Ghz (Python)
234 Fast-SSD 32.51 % 41.41 % 28.45 % 0.06 s GTX650Ti
235 centernet 32.22 % 35.79 % 28.50 % 0.01 s GPU @ 2.5 Ghz (Python)
236 RFCN_RFB 32.06 % 35.39 % 27.94 % 0.2 s 4 cores @ 2.5 Ghz (Python)
237 FailNet-Fusion
This method makes use of Velodyne laser scans.
31.68 % 30.84 % 30.56 % 0.1 s 1 core @ 2.5 Ghz (Python)
238 MTNAS 31.15 % 35.43 % 27.02 % 0.02 s 1 core @ 2.5 Ghz (python)
239 yolo800 31.13 % 32.49 % 26.76 % 0.13 s 4 cores @ 2.5 Ghz (Python)
240 FailNet-LIDAR
This method makes use of Velodyne laser scans.
31.10 % 30.32 % 29.89 % 0.1 s 1 core @ 2.5 Ghz (Python)
241 VoxelNet(Unofficial) 31.08 % 34.54 % 28.79 % 0.5 s GPU @ 2.0 Ghz (Python)
242 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)
243 RFCN 30.93 % 34.24 % 25.27 % 0.2 s 4 cores @ 2.5 Ghz (Python)
244 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.
245 m-prcnn
This method uses stereo information.
29.62 % 34.80 % 22.79 % 0.43 s 1 core @ 2.5 Ghz (Python)
246 DAM 28.97 % 37.05 % 25.28 % 1 s GPU @ 2.5 Ghz (Python)
247 fasterrcnn 28.42 % 30.28 % 24.95 % 0.2 s 4 cores @ 2.5 Ghz (Python)
248 RFBnet 27.91 % 34.44 % 25.24 % 0.2 s 4 cores @ 2.5 Ghz (Python)
249 E-VoxelNet 26.87 % 27.66 % 24.05 % 0.1 s GPU @ 2.5 Ghz (Python)
250 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.
251 Lidar_ROI+Yolo(UJS) 25.33 % 30.36 % 22.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
252 RADNet-Mono 24.78 % 28.55 % 22.84 % 0.1 s 1 core @ 2.5 Ghz (Python)
253 RT3D-GMP
This method uses stereo information.
24.27 % 28.33 % 18.51 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
254 100Frcnn 23.32 % 32.81 % 19.45 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
255 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.
256 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.
257 FailNet-Mono 19.63 % 25.13 % 17.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
258 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.
259 softyolo 18.31 % 26.80 % 15.28 % 0.16 s 4 cores @ 2.5 Ghz (Python)
260 Licar
This method makes use of Velodyne laser scans.
16.16 % 18.56 % 15.59 % 0.09 s GPU @ 2.0 Ghz (Python)
261 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.
262 KD53-20 13.76 % 20.58 % 11.91 % 0.19 s 4 cores @ 2.5 Ghz (Python)
263 Scan_YOLO 9.08 % 10.19 % 8.40 % 0.1 s 4 cores @ 3.0 Ghz (Python)
264 MuRF 1.75 % 0.63 % 2.14 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
265 MP 1.51 % 0.63 % 2.03 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
266 PiP 1.45 % 0.56 % 1.85 % 0.05 s 1 core @ 2.5 Ghz (Python)
267 Simple3D Net 1.38 % 0.63 % 1.76 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
268 SPA 1.25 % 0.59 % 1.64 % 0.1 s 1 core @ 2.5 Ghz (Python)
269 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. CVPR 2020.
270 FCPP 0.06 % 0.00 % 0.07 % 0.02 s 1 core @ 2.0 Ghz (Python + C/C++)
271 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 OHS 60.65 % 70.36 % 57.42 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
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 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.
18 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.
19 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.
20 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.
21 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.
22 3DBN_2 48.43 % 59.19 % 45.73 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
23 TBD 48.34 % 58.57 % 44.85 % 0.05 s GPU @ 2.5 Ghz (Python)
24 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.
25 PPFNet code 47.73 % 55.78 % 44.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
26 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.
27 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.
28 VOXEL_FPN_HR 45.65 % 56.17 % 42.10 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
29 HWFD 44.66 % 48.89 % 42.14 % 0.21 s one 1080Ti
30 SS3D_HW 44.43 % 59.56 % 38.77 % 0.4 s GPU @ 2.5 Ghz (Python)
31 FOFNet
This method makes use of Velodyne laser scans.
44.33 % 55.61 % 40.85 % 0.04 s GPU @ 2.5 Ghz (Python)
32 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.
33 DGIST-CellBox 43.86 % 48.68 % 41.52 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
34 DDB
This method makes use of Velodyne laser scans.
43.21 % 52.02 % 40.81 % 0.05 s GPU @ 2.5 Ghz (Python)
35 dgist_multiDetNet 43.17 % 48.19 % 40.84 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
36 PiP 42.76 % 51.23 % 40.06 % 0.05 s 1 core @ 2.5 Ghz (Python)
37 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.
38 Faster RCNN + Gr + A 40.92 % 47.81 % 37.89 % 1.29 s GPU @ 2.5 Ghz (Python)
39 HR-SECOND code 40.81 % 51.12 % 37.48 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
40 Faster RCNN + G 40.49 % 47.16 % 37.57 % 1.1 s GPU @ 2.5 Ghz (Python)
41 Faster RCNN + A 39.95 % 47.52 % 37.08 % 0.19 s GPU @ 2.5 Ghz (Python)
42 CentrNet-FG 39.88 % 47.51 % 37.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
43 CentrNet-v1
This method makes use of Velodyne laser scans.
39.83 % 46.21 % 38.05 % 0.03 s GPU @ 2.5 Ghz (Python)
44 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.
45 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.
46 Faster RCNN + A 39.44 % 46.80 % 36.46 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
47 CSFADet 38.41 % 46.75 % 35.44 % 0.05 s GPU @ 2.5 Ghz (Python)
48 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.
49 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++)
50 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. arXiv preprint arXiv:2003.05505 2020.
51 PP-3D 36.22 % 44.49 % 34.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
52 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.
53 SAANet 36.08 % 46.09 % 34.14 % 0.10 s 1 core @ 2.5 Ghz (Python)
54 AtrousDet 35.85 % 44.79 % 32.12 % 0.05 s TITAN X
55 Stereo3D
This method uses stereo information.
35.62 % 48.99 % 31.58 % 0.1 s GPU 1080Ti
56 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.
57 NL_M3D 35.20 % 46.64 % 30.56 % 0.2 s 1 core @ 2.5 Ghz (Python)
58 CRCNNA 34.88 % 43.18 % 31.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 FCY
This method makes use of Velodyne laser scans.
34.67 % 40.75 % 33.00 % 0.02 s GPU @ 2.5 Ghz (Python)
60 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.
61 merge12-12 34.10 % 43.60 % 30.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
62 cas+res+soft 34.01 % 43.51 % 30.28 % 0.2 s 4 cores @ 2.5 Ghz (Python)
63 cas_retina 33.98 % 43.80 % 31.12 % 0.2 s 4 cores @ 2.5 Ghz (Python)
64 cas_retina_1_13 33.87 % 43.55 % 30.99 % 0.03 s 4 cores @ 2.5 Ghz (Python)
65 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.
66 JSU-NET 33.55 % 45.79 % 30.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
67 DP3D 33.35 % 46.50 % 29.89 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
68 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.
69 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.
70 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.
71 DP3D 32.99 % 44.19 % 28.19 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
72 PG-MonoNet 32.67 % 44.75 % 29.33 % 0.19 s GPU @ 2.5 Ghz (Python)
73 cascadercnn 32.59 % 43.37 % 29.73 % 0.36 s 4 cores @ 2.5 Ghz (Python)
74 ReSqueeze 32.47 % 38.49 % 30.04 % 0.03 s GPU @ >3.5 Ghz (Python)
75 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.
76 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.
77 yolo800 32.12 % 40.53 % 28.83 % 0.13 s 4 cores @ 2.5 Ghz (Python)
78 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.
79 bin 31.94 % 36.94 % 29.50 % 15ms s GPU @ >3.5 Ghz (Python)
80 KNN-GCNN 31.91 % 39.25 % 29.76 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
81 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 .
82 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.
83 HG-Mono 31.62 % 43.63 % 28.33 % 0.46 s GPU @ 2.5 Ghz (C/C++)
84 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.
85 ZKNet 31.21 % 39.55 % 28.61 % 0.01 s GPU @ 2.0 Ghz (Python)
86 RFCN 30.97 % 40.51 % 27.45 % 0.2 s 4 cores @ 2.5 Ghz (Python)
87 LPN 30.84 % 38.60 % 28.49 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
88 DAM 30.58 % 41.32 % 27.84 % 1 s GPU @ 2.5 Ghz (Python)
89 CHTTL MMF 30.45 % 41.08 % 27.57 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
90 yolo4 30.09 % 40.84 % 27.35 % 0.02 s 1 core @ 2.5 Ghz (Python)
91 RFCN_RFB 29.91 % 38.71 % 26.50 % 0.2 s 4 cores @ 2.5 Ghz (Python)
92 deprecated 29.74 % 37.71 % 27.25 % 0.05 s GPU @ 2.0 Ghz (Python)
93 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.
94 fasterrcnn 29.48 % 38.63 % 26.89 % 0.2 s 4 cores @ 2.5 Ghz (Python)
95 FD2 28.40 % 35.59 % 25.75 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
96 MTDP 28.24 % 37.49 % 25.57 % 0.15 s GPU @ 2.0 Ghz (Python)
97 centernet 27.53 % 37.41 % 24.35 % 0.01 s GPU @ 2.5 Ghz (Python)
98 cascade_gw 26.32 % 36.41 % 23.73 % 0.2 s 4 cores @ 2.5 Ghz (Python)
99 Cmerge 25.09 % 34.53 % 22.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
100 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.
101 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.
102 Resnet101Faster rcnn 23.70 % 30.19 % 21.55 % 1 s 1 core @ 2.5 Ghz (Python)
103 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.
104 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.
105 OC Stereo
This method uses stereo information.
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.
106 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.
107 Retinanet100 21.71 % 29.72 % 19.12 % 0.2 s 4 cores @ 2.5 Ghz (Python)
108 softyolo 21.56 % 30.46 % 20.01 % 0.16 s 4 cores @ 2.5 Ghz (Python)
109 RT3D-GMP
This method uses stereo information.
20.81 % 29.49 % 18.34 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
110 Lidar_ROI+Yolo(UJS) 19.43 % 26.83 % 17.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
111 KD53-20 19.36 % 25.10 % 17.54 % 0.19 s 4 cores @ 2.5 Ghz (Python)
112 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.
113 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.
114 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.
115 100Frcnn 12.37 % 19.41 % 10.92 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
116 Simple3D Net 11.95 % 13.63 % 11.68 % 0.02 s GPU @ 2.5 Ghz (Python)
117 MP 5.39 % 6.41 % 5.14 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
118 CBNet 0.72 % 0.56 % 0.75 % 1 s 4 cores @ 2.5 Ghz (Python)
119 softretina 0.13 % 0.10 % 0.14 % 0.16 s 4 cores @ 2.5 Ghz (Python)
120 JSyolo 0.06 % 0.11 % 0.07 % 0.16 s 4 cores @ 2.5 Ghz (Python)
121 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 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.
2 OHS 78.31 % 85.79 % 71.24 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
3 78.23 % 85.65 % 71.60 %
4 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.
5 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.
6 TBD 76.79 % 87.00 % 70.00 % 0.05 s GPU @ 2.5 Ghz (Python)
7 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.
8 75.59 % 83.45 % 68.42 %
9 VOXEL_FPN_HR 74.77 % 87.41 % 68.16 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
10 MVX-Net++ 74.65 % 86.53 % 67.43 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
11 RethinkDet3D 74.33 % 88.54 % 65.20 % 0.15 s 1 core @ 2.5 Ghz (Python)
12 3DBN_2 73.69 % 87.96 % 66.91 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
13 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.
14 FOFNet
This method makes use of Velodyne laser scans.
72.48 % 86.89 % 65.63 % 0.04 s GPU @ 2.5 Ghz (Python)
15 PiP 71.10 % 82.83 % 64.88 % 0.05 s 1 core @ 2.5 Ghz (Python)
16 HR-SECOND code 69.60 % 82.42 % 62.47 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
17 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.
18 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.
19 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.
20 CentrNet-FG 66.68 % 82.23 % 59.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
21 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.
22 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++)
23 SAANet 65.52 % 82.29 % 58.81 % 0.10 s 1 core @ 2.5 Ghz (Python)
24 FCY
This method makes use of Velodyne laser scans.
64.64 % 80.76 % 58.05 % 0.02 s GPU @ 2.5 Ghz (Python)
25 Sogo_MM 63.50 % 71.57 % 55.24 % 1.5 s GPU @ 2.5 Ghz (C/C++)
26 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.
27 deprecated 63.08 % 83.73 % 53.51 % 0.05 s GPU @ 2.0 Ghz (Python)
28 CentrNet-v1
This method makes use of Velodyne laser scans.
62.11 % 78.10 % 55.54 % 0.03 s GPU @ 2.5 Ghz (Python)
29 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.
30 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.
31 PP-3D 60.09 % 76.73 % 53.41 % 0.1 s 1 core @ 2.5 Ghz (Python)
32 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.
33 DDB
This method makes use of Velodyne laser scans.
58.65 % 75.36 % 52.85 % 0.05 s GPU @ 2.5 Ghz (Python)
34 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.
35 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.
36 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.
37 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.
38 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.
39 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.
40 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.
41 Mono3CN 50.58 % 66.58 % 45.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 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.
43 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.
44 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.
45 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.
46 NL_M3D 41.19 % 57.44 % 36.24 % 0.2 s 1 core @ 2.5 Ghz (Python)
47 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.
48 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. arXiv preprint arXiv:2003.05505 2020.
49 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.
50 HG-Mono 38.48 % 54.04 % 32.01 % 0.46 s GPU @ 2.5 Ghz (C/C++)
51 SS3D_HW 37.68 % 52.40 % 32.33 % 0.4 s GPU @ 2.5 Ghz (Python)
52 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.
53 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.
54 PG-MonoNet 34.11 % 49.05 % 28.14 % 0.19 s GPU @ 2.5 Ghz (Python)
55 KNN-GCNN 34.03 % 39.32 % 31.17 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
56 HWFD 32.51 % 35.23 % 28.94 % 0.21 s one 1080Ti
57 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.
58 dgist_multiDetNet 32.22 % 36.52 % 28.66 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
59 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.
60 Faster RCNN + Gr + A 31.55 % 36.35 % 28.43 % 1.29 s GPU @ 2.5 Ghz (Python)
61 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 .
62 Faster RCNN + A 30.81 % 36.25 % 27.51 % 0.19 s GPU @ 2.5 Ghz (Python)
63 Faster RCNN + G 30.61 % 36.19 % 27.22 % 1.1 s GPU @ 2.5 Ghz (Python)
64 DGIST-CellBox 30.34 % 35.69 % 27.10 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
65 Faster RCNN + A 30.12 % 36.03 % 26.98 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
66 bin 29.63 % 35.40 % 25.98 % 15ms s GPU @ >3.5 Ghz (Python)
67 DP3D 28.41 % 42.17 % 24.02 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
68 AtrousDet 28.26 % 34.10 % 24.69 % 0.05 s TITAN X
69 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.
70 DP3D 27.47 % 40.80 % 24.16 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
71 ReSqueeze 27.40 % 36.26 % 24.04 % 0.03 s GPU @ >3.5 Ghz (Python)
72 cascadercnn 26.59 % 33.81 % 23.48 % 0.36 s 4 cores @ 2.5 Ghz (Python)
73 merge12-12 26.39 % 33.49 % 22.83 % 0.2 s 4 cores @ 2.5 Ghz (Python)
74 cas+res+soft 26.32 % 33.63 % 22.75 % 0.2 s 4 cores @ 2.5 Ghz (Python)
75 GA2500 26.08 % 32.91 % 22.06 % 0.2 s 1 core @ 2.5 Ghz (Python)
76 GA_rpn500 26.08 % 32.91 % 22.06 % 1 s 1 core @ 2.5 Ghz (Python)
77 DAM 26.05 % 34.25 % 22.30 % 1 s GPU @ 2.5 Ghz (Python)
78 GA_FULLDATA 25.80 % 33.35 % 22.70 % 1 s 4 cores @ 2.5 Ghz (Python)
79 CSFADet 25.77 % 32.19 % 22.78 % 0.05 s GPU @ 2.5 Ghz (Python)
80 GA_BALANCE 25.27 % 33.79 % 22.03 % 1 s 1 core @ 2.5 Ghz (Python)
81 cas_retina 25.24 % 31.74 % 22.30 % 0.2 s 4 cores @ 2.5 Ghz (Python)
82 cas_retina_1_13 25.01 % 31.17 % 22.12 % 0.03 s 4 cores @ 2.5 Ghz (Python)
83 bigger_ga 24.64 % 31.31 % 21.06 % 1 s 1 core @ 2.5 Ghz (Python)
84 CRCNNA 23.88 % 29.91 % 20.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 FD2 23.83 % 35.75 % 20.79 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
86 GAFM 22.84 % 31.62 % 19.88 % 0.5 s 1 core @ 2.5 Ghz (Python)
87 JSU-NET 22.83 % 31.58 % 19.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
88 yolo4 22.04 % 30.58 % 19.33 % 0.02 s 1 core @ 2.5 Ghz (Python)
89 ga50 21.59 % 29.77 % 18.77 % 1 s 1 core @ 2.5 Ghz (Python)
90 fasterrcnn 21.52 % 28.50 % 18.86 % 0.2 s 4 cores @ 2.5 Ghz (Python)
91 ZKNet 21.51 % 28.26 % 18.83 % 0.01 s GPU @ 2.0 Ghz (Python)
92 LPN 21.11 % 27.67 % 18.82 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
93 RFCN 20.77 % 26.80 % 18.25 % 0.2 s 4 cores @ 2.5 Ghz (Python)
94 yolo800 20.66 % 27.38 % 18.77 % 0.13 s 4 cores @ 2.5 Ghz (Python)
95 RFCN_RFB 20.40 % 26.19 % 17.91 % 0.2 s 4 cores @ 2.5 Ghz (Python)
96 DSGN
This method uses stereo information.
code 20.28 % 29.76 % 19.13 % 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.
97 Cmerge 19.78 % 27.75 % 16.58 % 0.2 s 4 cores @ 2.5 Ghz (Python)
98 LSVM-MDPM-sv 19.15 % 26.05 % 18.02 % 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.
99 OC Stereo
This method uses stereo information.
18.99 % 29.07 % 16.40 % 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.
100 DPM-VOC+VP 18.92 % 27.97 % 17.43 % 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.
101 cascade_gw 18.74 % 27.00 % 16.35 % 0.2 s 4 cores @ 2.5 Ghz (Python)
102 MTDP 18.02 % 23.30 % 16.07 % 0.15 s GPU @ 2.0 Ghz (Python)
103 BdCost+DA+BB+MS 17.73 % 23.48 % 14.67 % TBD s 4 cores @ 2.5 Ghz (C/C++)
104 centernet 17.55 % 23.39 % 15.59 % 0.01 s GPU @ 2.5 Ghz (Python)
105 RefinedMPL 16.02 % 26.54 % 13.20 % 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.
106 DPM-C8B1
This method uses stereo information.
14.64 % 23.93 % 13.09 % 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.
107 Retinanet100 13.34 % 19.09 % 11.79 % 0.2 s 4 cores @ 2.5 Ghz (Python)
108 BdCost+DA+BB 13.30 % 17.22 % 11.04 % TBD s 4 cores @ 2.5 Ghz (C/C++)
109 softyolo 11.12 % 15.91 % 9.84 % 0.16 s 4 cores @ 2.5 Ghz (Python)
110 100Frcnn 11.07 % 16.90 % 9.63 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
111 Lidar_ROI+Yolo(UJS) 8.95 % 13.15 % 7.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
112 RT3D-GMP
This method uses stereo information.
8.32 % 11.73 % 7.24 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
113 KD53-20 4.86 % 7.19 % 4.74 % 0.19 s 4 cores @ 2.5 Ghz (Python)
114 RT3DStereo
This method uses stereo information.
3.88 % 5.46 % 3.54 % 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.
115 Scan_YOLO 2.13 % 2.70 % 2.11 % 0.1 s 4 cores @ 3.0 Ghz (Python)
116 MP 0.97 % 0.62 % 0.89 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
117 Simple3D Net 0.71 % 0.77 % 0.69 % 0.02 s GPU @ 2.5 Ghz (Python)
118 CBNet 0.18 % 0.11 % 0.21 % 1 s 4 cores @ 2.5 Ghz (Python)
119 softretina 0.11 % 0.07 % 0.08 % 0.16 s 4 cores @ 2.5 Ghz (Python)
120 JSyolo 0.02 % 0.01 % 0.02 % 0.16 s 4 cores @ 2.5 Ghz (Python)
121 UM3D_TUM 0.00 % 0.00 % 0.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


Related Datasets

Citation

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



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