Bird's Eye View Evaluation 2017


The bird's eye view benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

We evaluate bird's eye view detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require a bounding box overlap of 70% in bird's eye view, while for pedestrians and cyclists we require an overlap of 50%. Difficulties are defined as follows:

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 SA-SSD code 91.03 % 95.03 % 85.96 % 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.
2 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 90.65 % 94.98 % 86.14 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
3 CN 90.50 % 94.51 % 85.86 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
4 Associate-3Ddet_v2 90.00 % 95.55 % 84.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
5 AIMC-RUC 89.80 % 93.64 % 84.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
6 OAP 89.72 % 93.13 % 82.25 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
7 D3D 89.72 % 93.37 % 84.72 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
8 NLK-3D 89.57 % 93.30 % 84.38 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
9 3D-CVF at SPA
This method makes use of Velodyne laser scans.
89.56 % 93.52 % 82.45 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
10 AAMF-SSD 89.54 % 95.26 % 82.31 % 0.05 s GPU @ 2.5 Ghz (Python)
11 Cas-SSD 89.47 % 93.31 % 84.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 PC-RGNN 89.47 % 95.36 % 84.29 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
13 STD code 89.19 % 94.74 % 86.42 % 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.
14 Point-GNN
This method makes use of Velodyne laser scans.
code 89.17 % 93.11 % 83.90 % 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.
15 Noah CV Lab - SSL 89.16 % 90.18 % 81.73 % 0.1 s GPU @ 2.5 Ghz (Python)
16 3DSSD code 89.02 % 92.66 % 85.86 % 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.
17 CLOCs_PointCas 88.99 % 92.60 % 81.74 % 0.1 s GPU @ 2.5 Ghz (Python)
18 Discrete-PointDet 88.95 % 94.56 % 83.56 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
19 HVNet 88.82 % 92.83 % 83.38 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
20 PointCSE 88.81 % 92.58 % 83.64 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
21 ELE 88.80 % 94.52 % 85.69 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
22 F-3DNet 88.76 % 92.68 % 83.63 % 0.5 s GPU @ 2.5 Ghz (Python)
23 FCPP 88.65 % 92.36 % 83.21 % 0.02 s 1 core @ 2.0 Ghz (Python + C/C++)
24 cvMax 88.64 % 92.12 % 83.72 % 0.04 s GPU @ >3.5 Ghz (Python)
25 deprecated 88.59 % 92.18 % 83.60 % 0.04 s GPU @ 2.5 Ghz (Python)
26 KNN-GCNN 88.57 % 91.73 % 83.32 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
27 PVF-NET 88.57 % 92.20 % 83.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
28 MuRF 88.56 % 91.57 % 83.46 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
29 DENFIDet 88.56 % 92.42 % 83.76 % 0.02 s GPU @ 2.5 Ghz (C/C++)
30 Chovy 88.54 % 92.34 % 83.68 % 0.04 s GPU @ 2.5 Ghz (Python)
31 EPNet 88.47 % 94.22 % 83.69 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
32 Patches
This method makes use of Velodyne laser scans.
88.39 % 92.72 % 83.19 % 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.
33 3D IoU-Net 88.38 % 94.76 % 81.93 % 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.
34 CLOCs_SecCas 88.23 % 91.16 % 82.63 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
35 UberATG-MMF
This method makes use of Velodyne laser scans.
88.21 % 93.67 % 81.99 % 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.
36 Patches - EMP
This method makes use of Velodyne laser scans.
88.17 % 94.49 % 84.75 % 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.
37 PointPainting
This method makes use of Velodyne laser scans.
88.11 % 92.45 % 83.36 % 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.
38 88.11 % 93.73 % 84.98 %
39 CPRCCNN 88.10 % 94.11 % 83.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
40 Associate-3Ddet code 88.09 % 91.40 % 82.96 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
41 OHS 88.09 % 94.06 % 83.24 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
42 DEFT 88.06 % 92.06 % 83.22 % 1 s GPU @ 2.5 Ghz (Python)
43 OneCoLab SicNet 88.06 % 92.17 % 83.60 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
44 deprecated 88.05 % 91.96 % 83.21 % 0.05 s GPU @ >3.5 Ghz (Python)
45 deprecated 88.04 % 91.97 % 83.22 % - -
46 UberATG-HDNET
This method makes use of Velodyne laser scans.
87.98 % 93.13 % 81.23 % 0.05 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
47 87.95 % 93.59 % 83.21 %
48 LZY_RCNN 87.94 % 91.74 % 83.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
49 SPA 87.90 % 91.70 % 83.18 % 0.1 s 1 core @ 2.5 Ghz (Python)
50 Fast Point R-CNN
This method makes use of Velodyne laser scans.
87.84 % 90.87 % 80.52 % 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.
51 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 87.79 % 91.70 % 84.61 % 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.
52 deprecated 87.63 % 93.66 % 80.35 % 0.06 s GPU @ >3.5 Ghz (Python)
53 MODet
This method makes use of Velodyne laser scans.
87.56 % 90.80 % 82.69 % 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.
54 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 87.53 % 91.99 % 81.03 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
55 PointRGCN 87.49 % 91.63 % 80.73 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
56 deprecated 87.46 % 92.54 % 77.39 % 0.05 s 1 core @ 2.5 Ghz (Python)
57 SAIC-SA-3D
This method makes use of Velodyne laser scans.
87.45 % 92.34 % 83.72 % 0.05 s GPU @ 2.5 Ghz (Python)
58 IE-PointRCNN 87.43 % 92.11 % 81.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 PC-CNN-V2
This method makes use of Velodyne laser scans.
87.40 % 91.19 % 79.35 % 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.
60 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 87.39 % 92.13 % 82.72 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
61 VAR 87.31 % 90.68 % 82.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 deprecated 87.28 % 90.44 % 75.09 % 0.05 s GPU @ 2.0 Ghz (Python)
63 PiP 87.25 % 90.87 % 83.38 % 0.033 s 1 core @ 2.5 Ghz (Python)
64 HRI-VoxelFPN 87.21 % 92.75 % 79.82 % 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.
65 CentrNet-v1
This method makes use of Velodyne laser scans.
87.19 % 90.72 % 83.34 % 0.03 s GPU @ 2.5 Ghz (Python)
66 MDA 87.13 % 90.67 % 82.80 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
67 epBRM
This method makes use of Velodyne laser scans.
code 87.13 % 90.70 % 81.92 % 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.
68 Roadstar.ai 87.12 % 92.42 % 81.88 % 0.08 s GPU @ 2.0 Ghz (Python)
69 Pointpillar_TV 87.08 % 90.50 % 81.98 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
70 EPENet 87.00 % 90.98 % 82.99 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
71 SARPNET 86.92 % 92.21 % 81.68 % 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.
72 ARPNET 86.81 % 90.06 % 79.41 % 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.
73 PointPiallars_SECA 86.79 % 90.15 % 82.87 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
74 C-GCN 86.78 % 91.11 % 80.09 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
75 FLID 86.77 % 91.58 % 81.14 % 0.04 s GPU @ 2.5 Ghz (Python)
76 CentrNet-FG 86.72 % 90.30 % 82.99 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
77 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
86.72 % 90.27 % 81.35 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
78 CU-PointRCNN 86.69 % 92.65 % 82.66 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
79 PointPillars
This method makes use of Velodyne laser scans.
code 86.56 % 90.07 % 82.81 % 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.
80 TANet code 86.54 % 91.58 % 81.19 % 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.
81 MVX-Net++ 86.53 % 91.86 % 81.41 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
82 PointRCNN-deprecated
This method makes use of Velodyne laser scans.
86.52 % 92.51 % 81.39 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
83 SCNet
This method makes use of Velodyne laser scans.
86.48 % 90.07 % 81.30 % 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.
84 RUC 86.46 % 90.06 % 82.20 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
85 Simple3D Net 86.46 % 89.82 % 82.60 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
86 DDB
This method makes use of Velodyne laser scans.
86.45 % 89.91 % 82.21 % 0.05 s GPU @ 2.5 Ghz (Python)
87 PPFNet code 86.44 % 92.35 % 81.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
88 autonet 86.42 % 89.81 % 81.25 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
89 HR-SECOND code 86.40 % 91.68 % 81.40 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
90 SECOND-V1.5
This method makes use of Velodyne laser scans.
code 86.37 % 91.81 % 81.04 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
91 SegVoxelNet 86.37 % 91.62 % 83.04 % 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.
92 VOXEL_FPN_HR 86.36 % 90.28 % 81.20 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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93 CP
This method makes use of Velodyne laser scans.
86.30 % 92.14 % 82.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
94 Bit 86.27 % 89.74 % 81.19 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
95 3D IoU Loss
This method makes use of Velodyne laser scans.
86.22 % 91.36 % 81.20 % 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.
96 IGRP 86.21 % 92.04 % 81.30 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
97 MP 86.16 % 90.24 % 78.86 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
98 FCY
This method makes use of Velodyne laser scans.
86.11 % 89.74 % 80.99 % 0.02 s GPU @ 2.5 Ghz (Python)
99 R-GCN 86.05 % 91.91 % 81.05 % 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.
100 RethinkDet3D 86.05 % 91.32 % 81.13 % 0.15 s 1 core @ 2.5 Ghz (Python)
101 UberATG-PIXOR++
This method makes use of Velodyne laser scans.
86.01 % 93.28 % 80.11 % 0.035 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
102 TBD 86.00 % 89.79 % 83.37 % 0.05 s GPU @ 2.5 Ghz (Python)
103 PPBA 85.85 % 91.30 % 80.92 % NA s GPU @ 2.5 Ghz (Python)
104 TBU 85.85 % 91.30 % 80.92 % NA s GPU @ 2.5 Ghz (Python)
105 F-ConvNet
This method makes use of Velodyne laser scans.
code 85.84 % 91.51 % 76.11 % 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.
106 RUC code 85.84 % 88.54 % 81.15 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
107 BVVF 85.83 % 91.20 % 80.76 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
108 PI-RCNN 85.81 % 91.44 % 81.00 % 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.
109 SAANet 85.69 % 91.72 % 78.77 % 0.10 s 1 core @ 2.5 Ghz (Python)
110 SFB-SECOND 85.63 % 91.38 % 78.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
111 PBASN code 85.62 % 90.95 % 80.49 % NA s GPU @ 2.5 Ghz (Python)
112 UberATG-ContFuse
This method makes use of Velodyne laser scans.
85.35 % 94.07 % 75.88 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
113 3DBN_2 85.30 % 91.37 % 82.57 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
114 AVOD
This method makes use of Velodyne laser scans.
code 84.95 % 89.75 % 78.32 % 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.
115 WS3D
This method makes use of Velodyne laser scans.
84.93 % 90.96 % 77.96 % 0.1 s GPU @ 2.5 Ghz (Python)
116 baseline 84.88 % 89.25 % 80.18 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
117 AVOD-FPN
This method makes use of Velodyne laser scans.
code 84.82 % 90.99 % 79.62 % 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.
118 Prune 84.81 % 90.48 % 77.40 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
119 autoRUC 84.80 % 90.44 % 77.43 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
120 F-PointNet
This method makes use of Velodyne laser scans.
code 84.67 % 91.17 % 74.77 % 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.
121 RUC code 84.40 % 89.11 % 79.33 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
122 MVSLN 84.26 % 90.30 % 78.94 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
123 PP-3D 84.11 % 89.16 % 76.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
124 3DBN
This method makes use of Velodyne laser scans.
83.94 % 89.66 % 76.50 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
125 RADNet-Fusion
This method makes use of Velodyne laser scans.
83.84 % 91.81 % 78.80 % 0.1 s 1 core @ 2.5 Ghz (Python)
126 RADNet-LIDAR
This method makes use of Velodyne laser scans.
83.74 % 92.43 % 77.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
127 3DNN 83.68 % 88.06 % 77.00 % 0.09 s GPU @ 2.5 Ghz (Python)
128 FailNet-Fusion
This method makes use of Velodyne laser scans.
82.78 % 93.20 % 75.84 % 0.1 s 1 core @ 2.5 Ghz (Python)
129 yl_net 82.70 % 87.27 % 80.23 % 0.03 s GPU @ 2.5 Ghz (Python)
130 MLOD
This method makes use of Velodyne laser scans.
code 82.68 % 90.25 % 77.97 % 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.
131 SECA 82.58 % 90.37 % 75.75 % 1 s GPU @ 2.5 Ghz (Python)
132 FailNet-LIDAR
This method makes use of Velodyne laser scans.
82.41 % 92.85 % 75.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
133 SDP-Net-s 81.93 % 86.57 % 75.83 % 12ms GPU @ 2.5 Ghz (Python)
134 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).
81.60 % 90.60 % 76.03 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
135 voxelrcnn 81.41 % 88.21 % 75.26 % 15 s 1 core @ 2.5 Ghz (C/C++)
136 RuiRUC 80.20 % 86.90 % 67.77 % 0.12 s 1 core @ 2.5 Ghz (Python)
137 UberATG-PIXOR
This method makes use of Velodyne laser scans.
80.01 % 83.97 % 74.31 % 0.035 s TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
138 NLK 79.15 % 82.59 % 72.65 % 0.02 s 1 core @ 2.5 Ghz (Python)
139 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
78.98 % 86.49 % 72.23 % 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.
140 MV3D
This method makes use of Velodyne laser scans.
78.93 % 86.62 % 69.80 % 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.
141 VoxelNet(Unofficial) 78.39 % 87.95 % 71.29 % 0.5 s GPU @ 2.0 Ghz (Python)
142 seivl 77.43 % 85.43 % 75.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
143 RCD 75.83 % 82.26 % 69.61 % 0.1 s GPU @ 2.5 Ghz (Python)
144 ANM 75.40 % 84.78 % 61.28 % 0.12 s 1 core @ 2.5 Ghz (Python)
145 LaserNet 74.52 % 79.19 % 68.45 % 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.
146 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 73.80 % 84.61 % 65.59 % 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.
147 anm 73.63 % 82.59 % 62.87 % 3 s 1 core @ 2.5 Ghz (C/C++)
148 A3DODWTDA
This method makes use of Velodyne laser scans.
code 73.26 % 79.58 % 62.77 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
149 avodC 72.78 % 84.61 % 66.02 % 0.1 s GPU @ 2.5 Ghz (Python)
150 E-VoxelNet 69.69 % 81.10 % 60.88 % 0.1 s GPU @ 2.5 Ghz (Python)
151 Complexer-YOLO
This method makes use of Velodyne laser scans.
68.96 % 77.24 % 64.95 % 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.
152 TopNet-Retina
This method makes use of Velodyne laser scans.
68.16 % 80.16 % 63.43 % 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.
153 CG-Stereo
This method uses stereo information.
66.44 % 85.29 % 58.95 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
154 CDN
This method uses stereo information.
66.24 % 83.32 % 57.65 % 0.6 s GPU @ 2.5 Ghz (Python)
155 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
65.74 % 74.20 % 58.35 % 0.5 s 1 core @ 2.5 Ghz (Python)
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156 DSGN
This method uses stereo information.
code 65.05 % 82.90 % 56.60 % 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.
157 TopNet-DecayRate
This method makes use of Velodyne laser scans.
64.60 % 79.74 % 58.04 % 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.
158 BirdNet+
This method makes use of Velodyne laser scans.
code 63.33 % 84.80 % 61.23 % 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.
159 3D FCN
This method makes use of Velodyne laser scans.
61.67 % 70.62 % 55.61 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
160 CDN-PL++
This method uses stereo information.
61.04 % 81.27 % 52.84 % 0.4 s GPU @ 2.5 Ghz (C/C++)
161 BirdNet
This method makes use of Velodyne laser scans.
59.83 % 84.17 % 57.35 % 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.
162 TopNet-UncEst
This method makes use of Velodyne laser scans.
59.67 % 72.05 % 51.67 % 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.
163 Pseudo-LiDAR E2E
This method uses stereo information.
58.84 % 79.58 % 52.06 % 0.4 s GPU @ 2.5 Ghz (Python)
164 PB3D
This method uses stereo information.
58.04 % 79.75 % 49.78 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
165 Pseudo-LiDAR++
This method uses stereo information.
code 58.01 % 78.31 % 51.25 % 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.
166 ZoomNet
This method uses stereo information.
code 54.91 % 72.94 % 44.14 % 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.
167 VoxelJones code 53.96 % 66.21 % 47.66 % .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.
168 TopNet-HighRes
This method makes use of Velodyne laser scans.
53.05 % 67.84 % 46.99 % 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.
169 Disp R-CNN
This method uses stereo information.
code 52.37 % 73.87 % 43.67 % 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.
170 Disp R-CNN (velo)
This method uses stereo information.
code 52.37 % 74.12 % 43.79 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
171 OC Stereo
This method uses stereo information.
code 51.47 % 68.89 % 42.97 % 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.
172 Stereo3D
This method uses stereo information.
50.28 % 76.10 % 36.86 % 0.1 s GPU 1080Ti
173 stereo_sa
This method uses stereo information.
49.61 % 71.47 % 42.71 % 0.3 s GPU @ 2.5 Ghz (Python)
174 RT3D-GMP
This method uses stereo information.
49.57 % 61.28 % 38.70 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
175 RT3DStereo
This method uses stereo information.
46.82 % 58.81 % 38.38 % 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.
176 Pseudo-Lidar
This method uses stereo information.
code 45.00 % 67.30 % 38.40 % 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.
177 RT3D
This method makes use of Velodyne laser scans.
44.00 % 56.44 % 42.34 % 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.
178 m-prcnn
This method uses stereo information.
42.81 % 67.82 % 33.63 % 0.43 s 1 core @ 2.5 Ghz (Python)
179 IDA-3D
This method uses stereo information.
42.47 % 61.87 % 34.59 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
180 Stereo R-CNN
This method uses stereo information.
code 41.31 % 61.92 % 33.42 % 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.
181 Licar
This method makes use of Velodyne laser scans.
38.47 % 46.67 % 35.78 % 0.09 s GPU @ 2.0 Ghz (Python)
182 ASOD 33.63 % 54.61 % 26.76 % 0.28 s GPU @ 2.5 Ghz (Python)
183 StereoFENet
This method uses stereo information.
32.96 % 49.29 % 25.90 % 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.
184 deprecated 30.56 % 34.56 % 25.69 % 1 core @ 2.5 Ghz (C/C++)
185 S3D 30.44 % 35.25 % 25.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
186 LNET 29.68 % 34.30 % 25.11 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
187 PSMD 19.33 % 28.63 % 15.31 % 0.1 s GPU @ 2.5 Ghz (Python)
188 MTMono3d 18.54 % 27.00 % 15.71 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
189 IAFA 17.88 % 25.88 % 15.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
190 RefinedMPL 17.60 % 28.08 % 13.95 % 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.
191 AM3D 17.32 % 25.03 % 14.91 % 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.
192 YoloMono3D 17.15 % 26.79 % 12.56 % 0.05 s GPU @ 2.5 Ghz (Python)
193 IMA 17.08 % 23.93 % 14.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
194 MCA 17.07 % 25.93 % 14.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
195 DP3D 16.96 % 26.51 % 12.82 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
196 PatchNet 16.86 % 22.97 % 14.97 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
197 UM3D_TUM 16.69 % 23.63 % 14.17 % 0.05 s 1 core @ 2.5 Ghz (Python)
198 ITS-MDPL 16.64 % 28.94 % 14.17 % 0.16 s GPU @ 2.5 Ghz (Python)
199 PG-MonoNet 16.31 % 23.31 % 13.03 % 0.19 s GPU @ 2.5 Ghz (Python)
200 SSL-RTM3D 16.20 % 23.44 % 14.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
201 D4LCN code 16.02 % 22.51 % 12.55 % 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.
202 NL_M3D 15.93 % 24.15 % 12.11 % 0.2 s 1 core @ 2.5 Ghz (Python)
203 DA-3Ddet 15.90 % 23.35 % 12.11 % 0.4 s GPU @ 2.5 Ghz (Python)
204 HG-Mono 15.86 % 22.96 % 12.02 % 0.46 s GPU @ 2.5 Ghz (C/C++)
205 DP3D 15.44 % 23.98 % 12.24 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
206 MA 15.43 % 22.01 % 14.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
207 MonoPair 14.83 % 19.28 % 12.89 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
208 Decoupled-3D 14.82 % 23.16 % 11.25 % 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.
209 Decoupled-3D v2 14.66 % 24.62 % 11.46 % 0.08 s GPU @ 2.5 Ghz (C/C++)
210 MonoSS 14.52 % 20.91 % 12.63 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
211 SMOKE code 14.49 % 20.83 % 12.75 % 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.
212 RTM3D code 14.20 % 19.17 % 11.99 % 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.
213 Mono3CN 14.17 % 19.82 % 12.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
214 LCD3D 13.99 % 21.97 % 11.43 % 0.03 s GPU @ 2.5 Ghz (Python)
215 Mono3D_PLiDAR code 13.92 % 21.27 % 11.25 % 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.
216 SS3D_HW 13.70 % 20.28 % 9.86 % 0.4 s GPU @ 2.5 Ghz (Python)
217 M3D-RPN code 13.67 % 21.02 % 10.23 % 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 .
218 SSL-RTM3D Res18 13.37 % 19.71 % 11.10 % 0.02 s GPU @ 2.5 Ghz (Python)
219 CSoR
This method makes use of Velodyne laser scans.
13.07 % 18.67 % 10.34 % 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.
220 RAR-Net 13.01 % 20.63 % 10.19 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
221 MonoPSR code 12.58 % 18.33 % 9.91 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
222 SS3D 11.52 % 16.33 % 9.93 % 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.
223 MonoGRNet code 11.17 % 18.19 % 8.73 % 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.
224 MonoFENet 11.03 % 17.03 % 9.05 % 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.
225 anonymous 10.96 % 20.42 % 9.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
226 RADNet-Mono 10.57 % 15.22 % 8.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
227 OACV 10.13 % 16.24 % 8.28 % 0.23 s GPU @ 2.5 Ghz (Python)
228 anonymous 10.06 % 18.80 % 8.56 % 1 s 1 core @ 2.5 Ghz (C/C++)
229 FailNet-Mono 9.11 % 14.41 % 7.11 % 0.1 s 1 core @ 2.5 Ghz (Python)
230 A3DODWTDA (image) code 8.66 % 10.37 % 7.06 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
231 TLNet (Stereo)
This method uses stereo information.
code 7.69 % 13.71 % 6.73 % 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.
232 Shift R-CNN (mono) code 6.82 % 11.84 % 5.27 % 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.
233 AACL 6.75 % 8.55 % 5.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
234 GS3D 6.08 % 8.41 % 4.94 % 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.
235 MVRA + I-FRCNN+ 5.84 % 9.05 % 4.50 % 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.
236 ROI-10D 4.91 % 9.78 % 3.74 % 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.
237 3D-GCK 4.57 % 5.79 % 3.64 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
238 FQNet 3.23 % 5.40 % 2.46 % 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.
239 3D-SSMFCNN code 2.63 % 3.20 % 2.40 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
240 monoref3d 1.70 % 3.22 % 1.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
241 ref3D 1.70 % 3.22 % 1.21 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
242 3DVSSD 1.31 % 1.74 % 1.08 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
243 VeloFCN
This method makes use of Velodyne laser scans.
0.14 % 0.02 % 0.21 % 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 .
244 ANM 0.00 % 0.00 % 0.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
245 ref3D 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
246 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
247 multi-task CNN 0.00 % 0.00 % 0.00 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
248 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 DENFIDet 51.96 % 61.15 % 49.03 % 0.02 s GPU @ 2.5 Ghz (C/C++)
2 TANet code 51.38 % 60.85 % 47.54 % 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.
3 CentrNet-FG 50.87 % 60.56 % 48.16 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
4 Noah CV Lab - SSL 50.66 % 57.27 % 46.55 % 0.1 s GPU @ 2.5 Ghz (Python)
5 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 50.57 % 59.86 % 46.74 % 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.
6 OHS 50.53 % 57.39 % 46.65 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
7 VMVS
This method makes use of Velodyne laser scans.
50.34 % 60.34 % 46.45 % 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.
8 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.32 % 58.49 % 46.98 % 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.
9 3DSSD code 49.94 % 60.54 % 45.73 % 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.
10 PointPainting
This method makes use of Velodyne laser scans.
49.93 % 58.70 % 46.29 % 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.
11 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 49.81 % 59.04 % 45.92 % 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.
12 F-PointNet
This method makes use of Velodyne laser scans.
code 49.57 % 57.13 % 45.48 % 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.
13 49.48 % 55.90 % 45.79 %
14 PPBA 49.34 % 57.23 % 46.86 % NA s GPU @ 2.5 Ghz (Python)
15 F-ConvNet
This method makes use of Velodyne laser scans.
code 48.96 % 57.04 % 44.33 % 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.
16 HVNet 48.86 % 54.84 % 46.33 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
17 RethinkDet3D 48.84 % 58.96 % 46.20 % 0.15 s 1 core @ 2.5 Ghz (Python)
18 CentrNet-v1
This method makes use of Velodyne laser scans.
48.78 % 57.58 % 45.94 % 0.03 s GPU @ 2.5 Ghz (Python)
19 STD code 48.72 % 60.02 % 44.55 % 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.
20 PointPillars
This method makes use of Velodyne laser scans.
code 48.64 % 57.60 % 45.78 % 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.
21 DDB
This method makes use of Velodyne laser scans.
48.35 % 57.68 % 45.44 % 0.05 s GPU @ 2.5 Ghz (Python)
22 PiP 48.14 % 56.16 % 45.27 % 0.033 s 1 core @ 2.5 Ghz (Python)
23 MVX-Net++ 48.04 % 56.63 % 45.44 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
24 PPFNet code 47.92 % 55.04 % 44.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
25 LDAM 47.35 % 52.08 % 45.23 % 24 ms GTX 1080 ti GPU
26 Simple3D Net 47.27 % 56.05 % 44.70 % 0.02 s GPU @ 2.5 Ghz (Python)
27 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
47.24 % 56.06 % 44.61 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
28 Point-GNN
This method makes use of Velodyne laser scans.
code 47.07 % 55.36 % 44.61 % 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.
29 KNN-GCNN 46.77 % 55.11 % 44.43 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
30 TBU 46.76 % 55.15 % 44.60 % NA s GPU @ 2.5 Ghz (Python)
31 PP-3D 46.74 % 56.74 % 44.01 % 0.1 s 1 core @ 2.5 Ghz (Python)
32 SCNet
This method makes use of Velodyne laser scans.
46.73 % 56.87 % 42.74 % 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.
33 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 46.13 % 54.77 % 42.84 % 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.
34 ARPNET 45.92 % 55.48 % 42.54 % 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.
35 epBRM
This method makes use of Velodyne laser scans.
code 45.49 % 52.48 % 42.75 % 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.
36 MLOD
This method makes use of Velodyne laser scans.
code 45.40 % 55.09 % 41.42 % 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.
37 44.59 % 50.87 % 42.14 %
38 Roadstar.ai 44.35 % 49.63 % 41.39 % 0.08 s GPU @ 2.0 Ghz (Python)
39 FCY
This method makes use of Velodyne laser scans.
43.88 % 51.21 % 41.41 % 0.02 s GPU @ 2.5 Ghz (Python)
40 3DBN_2 42.97 % 50.99 % 40.49 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
41 VOXEL_FPN_HR 41.62 % 50.18 % 38.30 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
42 deprecated 41.32 % 53.09 % 37.16 % 0.05 s GPU @ 2.0 Ghz (Python)
43 TBD 41.12 % 48.24 % 39.06 % 0.05 s GPU @ 2.5 Ghz (Python)
44 PBASN code 40.63 % 46.80 % 38.41 % NA s GPU @ 2.5 Ghz (Python)
45 HR-SECOND code 40.06 % 50.05 % 36.47 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
46 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 38.79 % 47.51 % 35.85 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
47 MP 38.77 % 47.59 % 35.50 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
48 BirdNet+
This method makes use of Velodyne laser scans.
code 38.28 % 45.53 % 35.37 % 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.
49 anm 38.01 % 49.07 % 34.00 % 3 s 1 core @ 2.5 Ghz (C/C++)
50 CSW3D
This method makes use of Velodyne laser scans.
37.96 % 49.27 % 33.83 % 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.
51 Pointpillar_TV 35.28 % 42.65 % 33.10 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
52 SparsePool code 34.15 % 43.33 % 31.78 % 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.
53 SAANet 33.94 % 42.34 % 31.75 % 0.10 s 1 core @ 2.5 Ghz (Python)
54 AVOD
This method makes use of Velodyne laser scans.
code 33.57 % 42.58 % 30.14 % 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.
55 SparsePool code 33.22 % 41.55 % 29.66 % 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.
56 32.32 % 40.87 % 29.52 %
57 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
29.77 % 37.16 % 26.61 % 0.5 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
58 CG-Stereo
This method uses stereo information.
29.56 % 39.24 % 25.87 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
59 Disp R-CNN
This method uses stereo information.
code 25.36 % 36.06 % 21.62 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
60 Disp R-CNN (velo)
This method uses stereo information.
code 24.95 % 35.39 % 21.30 % 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.
61 PB3D
This method uses stereo information.
23.62 % 33.00 % 20.35 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
62 BirdNet
This method makes use of Velodyne laser scans.
23.06 % 28.20 % 21.65 % 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.
63 OC Stereo
This method uses stereo information.
code 20.80 % 29.79 % 18.62 % 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.
64 Stereo3D
This method uses stereo information.
20.76 % 31.01 % 18.41 % 0.1 s GPU 1080Ti
65 DSGN
This method uses stereo information.
code 20.75 % 26.61 % 18.86 % 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.
66 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.26 % 21.42 % 17.06 % 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.
67 TopNet-Retina
This method makes use of Velodyne laser scans.
14.57 % 18.04 % 12.48 % 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.
68 TopNet-HighRes
This method makes use of Velodyne laser scans.
13.50 % 19.43 % 11.93 % 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.
69 RefinedMPL 7.92 % 13.09 % 7.25 % 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.
70 MonoPair 7.04 % 10.99 % 6.29 % 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.
71 TopNet-DecayRate
This method makes use of Velodyne laser scans.
6.59 % 8.78 % 6.25 % 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.
72 RT3D-GMP
This method uses stereo information.
5.73 % 7.93 % 5.62 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
73 Shift R-CNN (mono) code 5.66 % 8.58 % 4.49 % 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.
74 SS3D_HW 5.47 % 8.81 % 4.79 % 0.4 s GPU @ 2.5 Ghz (Python)
75 PG-MonoNet 5.43 % 7.06 % 4.55 % 0.19 s GPU @ 2.5 Ghz (Python)
76 NL_M3D 4.66 % 6.20 % 3.99 % 0.2 s 1 core @ 2.5 Ghz (Python)
77 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.60 % 6.88 % 3.79 % 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.
78 MonoPSR code 4.56 % 7.24 % 4.11 % 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.
79 M3D-RPN code 4.05 % 5.65 % 3.29 % 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 .
80 Mono3CN 4.02 % 6.03 % 3.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 DP3D 4.01 % 5.71 % 3.64 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
82 HG-Mono 3.90 % 5.66 % 3.67 % 0.46 s GPU @ 2.5 Ghz (C/C++)
83 DP3D 3.86 % 5.25 % 3.10 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
84 D4LCN code 3.86 % 5.06 % 3.59 % 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.
85 RT3DStereo
This method uses stereo information.
3.65 % 4.72 % 3.00 % 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.
86 MTMono3d 2.38 % 3.11 % 1.89 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
87 SS3D 2.09 % 2.48 % 1.61 % 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.
88 UM3D_TUM 1.79 % 3.60 % 1.79 % 0.05 s 1 core @ 2.5 Ghz (Python)
89 PVNet 0.01 % 0.00 % 0.01 % 0,1 s 1 core @ 2.5 Ghz (Python)
90 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 Noah CV Lab - SSL 74.45 % 85.96 % 64.23 % 0.1 s GPU @ 2.5 Ghz (Python)
2 PointPainting
This method makes use of Velodyne laser scans.
71.54 % 83.91 % 62.97 % 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.
3 HVNet 71.17 % 83.97 % 63.65 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
4 TBD 69.08 % 83.68 % 62.28 % 0.05 s GPU @ 2.5 Ghz (Python)
5 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 68.89 % 82.49 % 62.41 % 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.
6 F-ConvNet
This method makes use of Velodyne laser scans.
code 68.88 % 84.16 % 60.05 % 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.
7 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 68.73 % 83.43 % 61.85 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
8 OHS 68.51 % 83.29 % 61.84 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
9 3DSSD code 67.62 % 85.04 % 61.14 % 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.
10 PPBA 67.28 % 82.69 % 60.53 % NA s GPU @ 2.5 Ghz (Python)
11 TBU 67.28 % 82.69 % 60.53 % NA s GPU @ 2.5 Ghz (Python)
12 Point-GNN
This method makes use of Velodyne laser scans.
code 67.28 % 81.17 % 59.67 % 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.
13 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 67.24 % 82.56 % 60.28 % 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 STD code 67.23 % 81.36 % 59.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
15 KNN-GCNN 67.22 % 83.35 % 59.51 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
16 67.20 % 79.66 % 61.04 %
17 66.86 % 82.13 % 60.86 %
18 RethinkDet3D 66.42 % 82.73 % 59.60 % 0.15 s 1 core @ 2.5 Ghz (Python)
19 ARPNET 66.39 % 82.32 % 58.80 % 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.
20 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 65.85 % 80.00 % 58.69 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
21 DENFIDet 65.49 % 82.13 % 57.76 % 0.02 s GPU @ 2.5 Ghz (C/C++)
22 PiP 65.12 % 79.51 % 58.25 % 0.033 s 1 core @ 2.5 Ghz (Python)
23 VOXEL_FPN_HR 65.02 % 81.07 % 58.44 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
24 MVX-Net++ 64.84 % 78.89 % 58.15 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
25 3DBN_2 64.28 % 81.06 % 57.55 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
26 HR-SECOND code 64.21 % 78.79 % 57.82 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
27 TANet code 63.77 % 79.16 % 56.21 % 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.
28 PBASN code 63.34 % 79.45 % 57.01 % NA s GPU @ 2.5 Ghz (Python)
29 LDAM 63.17 % 77.22 % 57.34 % 24 ms GTX 1080 ti GPU
30 PointPillars
This method makes use of Velodyne laser scans.
code 62.73 % 79.90 % 55.58 % 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.
31 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
62.34 % 78.91 % 55.37 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
32 CentrNet-FG 62.10 % 76.94 % 54.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
33 F-PointNet
This method makes use of Velodyne laser scans.
code 61.37 % 77.26 % 53.78 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
34 MP 60.16 % 77.57 % 54.01 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
35 epBRM
This method makes use of Velodyne laser scans.
code 59.79 % 75.13 % 53.36 % 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.
36 Roadstar.ai 59.50 % 68.73 % 53.60 % 0.08 s GPU @ 2.0 Ghz (Python)
37 FCY
This method makes use of Velodyne laser scans.
59.35 % 76.73 % 52.63 % 0.02 s GPU @ 2.5 Ghz (Python)
38 Pointpillar_TV 59.26 % 74.78 % 52.33 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
39 Simple3D Net 59.03 % 75.72 % 52.42 % 0.02 s GPU @ 2.5 Ghz (Python)
40 CentrNet-v1
This method makes use of Velodyne laser scans.
58.05 % 75.80 % 51.17 % 0.03 s GPU @ 2.5 Ghz (Python)
41 SAANet 57.98 % 74.71 % 51.95 % 0.10 s 1 core @ 2.5 Ghz (Python)
42 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.12 % 69.39 % 51.09 % 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.
43 DDB
This method makes use of Velodyne laser scans.
57.01 % 73.70 % 50.71 % 0.05 s GPU @ 2.5 Ghz (Python)
44 deprecated 56.42 % 81.02 % 49.28 % 0.05 s GPU @ 2.0 Ghz (Python)
45 SCNet
This method makes use of Velodyne laser scans.
56.39 % 73.73 % 49.99 % 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.
46 PP-3D 55.06 % 71.94 % 48.10 % 0.1 s 1 core @ 2.5 Ghz (Python)
47 MLOD
This method makes use of Velodyne laser scans.
code 55.06 % 73.03 % 48.21 % 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.
48 BirdNet+
This method makes use of Velodyne laser scans.
code 52.15 % 72.45 % 46.57 % 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.
49 AVOD
This method makes use of Velodyne laser scans.
code 48.15 % 64.11 % 42.37 % 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.
50 BirdNet
This method makes use of Velodyne laser scans.
41.56 % 58.64 % 36.94 % 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.
51 SparsePool code 40.74 % 56.52 % 36.68 % 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.
52 anm 38.56 % 56.94 % 34.06 % 3 s 1 core @ 2.5 Ghz (C/C++)
53 TopNet-Retina
This method makes use of Velodyne laser scans.
36.83 % 47.48 % 33.58 % 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.
54 CG-Stereo
This method uses stereo information.
36.25 % 55.33 % 32.17 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
55 SparsePool code 35.24 % 43.55 % 30.15 % 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.
56 Disp R-CNN (velo)
This method uses stereo information.
code 26.46 % 43.41 % 22.46 % 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.
57 Disp R-CNN
This method uses stereo information.
code 26.46 % 43.41 % 22.46 % 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.
58 Complexer-YOLO
This method makes use of Velodyne laser scans.
25.43 % 32.00 % 22.88 % 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.
59 DSGN
This method uses stereo information.
code 21.04 % 31.23 % 18.93 % 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.
60 PB3D
This method uses stereo information.
19.41 % 32.06 % 17.42 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
61 OC Stereo
This method uses stereo information.
code 19.23 % 32.47 % 17.11 % 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.
62 TopNet-DecayRate
This method makes use of Velodyne laser scans.
16.00 % 23.02 % 13.24 % 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.
63 TopNet-UncEst
This method makes use of Velodyne laser scans.
9.18 % 12.31 % 8.14 % 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.
64 RT3D-GMP
This method uses stereo information.
6.90 % 10.09 % 6.14 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
65 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.48 % 9.99 % 6.76 % 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.
66 MonoPSR code 5.78 % 9.87 % 4.57 % 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.
67 RT3DStereo
This method uses stereo information.
4.10 % 7.03 % 3.88 % 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.
68 MonoPair 2.87 % 4.76 % 2.42 % 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.
69 SS3D_HW 2.78 % 5.03 % 2.36 % 0.4 s GPU @ 2.5 Ghz (Python)
70 Mono3CN 2.69 % 3.92 % 2.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 RefinedMPL 2.42 % 4.23 % 2.14 % 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.
72 NL_M3D 2.01 % 2.70 % 1.75 % 0.2 s 1 core @ 2.5 Ghz (Python)
73 PG-MonoNet 1.89 % 3.00 % 1.66 % 0.19 s GPU @ 2.5 Ghz (Python)
74 SS3D 1.89 % 3.45 % 1.44 % 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.
75 DP3D 1.87 % 3.09 % 1.96 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
76 D4LCN code 1.82 % 2.72 % 1.79 % 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.
77 DP3D 1.57 % 2.32 % 1.29 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
78 MTMono3d 1.30 % 2.06 % 1.06 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
79 HG-Mono 1.27 % 2.16 % 1.34 % 0.46 s GPU @ 2.5 Ghz (C/C++)
80 M3D-RPN code 0.81 % 1.25 % 0.78 % 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 .
81 UM3D_TUM 0.62 % 0.45 % 0.62 % 0.05 s 1 core @ 2.5 Ghz (Python)
82 Shift R-CNN (mono) code 0.38 % 0.76 % 0.41 % 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.
83 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
84 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Related Datasets

Citation

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



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