3D Object Detection Evaluation 2017


The 3D object detection benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

We evaluate 3D object detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require an 3D bounding box overlap of 70%, while for pedestrians and cyclists we require a 3D bounding box overlap of 50%. Difficulties are defined as follows:

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 81.43 % 90.25 % 76.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
2 CN 79.89 % 90.55 % 76.31 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
3 SA-SSD code 79.79 % 88.75 % 74.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
4 3D-CVF code 79.72 % 88.84 % 72.80 % 0.06 s GPU @ >3.5 Ghz (Python)
5 STD 79.71 % 87.95 % 75.09 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
6 3DSSD 79.57 % 88.36 % 74.55 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
7 Point-GNN
This method makes use of Velodyne laser scans.
code 79.47 % 88.33 % 72.29 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
8 EPNet 79.28 % 89.81 % 74.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
9 3D IoU-Net 79.03 % 87.96 % 72.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 Noah CV Lab - SSL 78.99 % 85.50 % 71.75 % 0.1 s GPU @ 2.5 Ghz (Python)
11 CPRCCNN 78.96 % 87.74 % 74.30 % 0.1 s 1 core @ 2.5 Ghz (Python)
12 Discrete-PointDet 78.51 % 88.53 % 71.29 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
13 ORP 78.50 % 87.38 % 71.49 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
14 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.49 % 87.81 % 73.51 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
15 F-3DNet 78.48 % 85.48 % 71.62 % 0.5 s GPU @ 2.5 Ghz (Python)
16 CLOCs_SecCas 78.45 % 86.38 % 72.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
17 Patches - EMP
This method makes use of Velodyne laser scans.
78.41 % 89.84 % 73.15 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
18 ELE 78.35 % 86.95 % 73.33 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
19 78.34 % 88.12 % 73.49 %
20 OHS 78.31 % 87.60 % 73.34 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
21 HRI-FusionRCNN 78.29 % 88.46 % 70.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 cvMax 78.28 % 86.60 % 71.60 % 0.04 s GPU @ >3.5 Ghz (Python)
23 KNN-GCNN 78.26 % 86.37 % 71.14 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
24 CP
This method makes use of Velodyne laser scans.
78.11 % 86.40 % 71.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 Chovy 78.02 % 86.86 % 73.20 % 0.04 s GPU @ 2.5 Ghz (Python)
26 deprecated 77.97 % 86.76 % 73.00 % 0.04 s GPU @ 2.5 Ghz (Python)
27 deprecated 77.97 % 86.53 % 67.90 % 0.05 s 1 core @ 2.5 Ghz (Python)
28 77.74 % 86.40 % 72.97 %
29 PPBA 77.65 % 84.16 % 71.21 % NA s GPU @ 2.5 Ghz (Python)
30 TBU 77.65 % 84.16 % 71.21 % NA s GPU @ 2.5 Ghz (Python)
31 deprecated 77.62 % 86.21 % 67.68 % 0.05 s GPU @ 2.0 Ghz (Python)
32 UberATG-MMF
This method makes use of Velodyne laser scans.
77.43 % 88.40 % 70.22 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
33 Associate-3Ddet code 77.40 % 85.99 % 70.53 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
D. Liang*, Y. Xiaoqing*, T. Xiao, F. Jianfeng, X. Zhenbo, D. Errui and W. Shilei: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. CVPR 2020.
34 Fast Point R-CNN
This method makes use of Velodyne laser scans.
77.40 % 85.29 % 70.24 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
35 deprecated 77.31 % 86.44 % 70.91 % - -
36 Patches
This method makes use of Velodyne laser scans.
77.20 % 88.67 % 71.82 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
37 deprecated 77.17 % 86.27 % 70.83 % 0.05 s GPU @ >3.5 Ghz (Python)
38 DEFT 77.15 % 86.34 % 70.76 % 1 s GPU @ 2.5 Ghz (Python)
39 CU-PointRCNN 76.87 % 86.55 % 73.17 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
40 HRI-VoxelFPN 76.70 % 85.64 % 69.44 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
41 CLOCs_PointCas 76.68 % 87.50 % 71.21 % 0.1 s GPU @ 2.5 Ghz (Python)
42 SARPNET 76.64 % 85.63 % 71.31 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
43 SRF 76.61 % 86.63 % 71.28 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
44 3D IoU Loss
This method makes use of Velodyne laser scans.
76.50 % 86.16 % 71.39 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
45 F-ConvNet
This method makes use of Velodyne laser scans.
code 76.39 % 87.36 % 66.69 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
46 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
76.29 % 84.71 % 69.18 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
47 RGB3D
This method makes use of Velodyne laser scans.
76.26 % 87.26 % 71.16 % 0.39 s GPU @ 2.5 Ghz (Python)
48 PiP 76.24 % 85.30 % 70.45 % 0.05 s 1 core @ 2.5 Ghz (Python)
49 SegVoxelNet 76.13 % 86.04 % 70.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
50 PTS
This method makes use of Velodyne laser scans.
code 76.04 % 84.51 % 70.77 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
51 SECOND-V1.5
This method makes use of Velodyne laser scans.
code 75.96 % 84.65 % 68.71 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
52 TANet code 75.94 % 84.39 % 68.82 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
53 MMV 75.91 % 84.46 % 68.69 % 0.4 s GPU @ 2.5 Ghz (C/C++)
54 MVX-Net++ 75.86 % 85.99 % 70.70 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
55 PointCSE 75.82 % 86.46 % 70.47 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
56 PointRCNN-deprecated
This method makes use of Velodyne laser scans.
75.81 % 86.23 % 68.99 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
57 CentrNet-v1
This method makes use of Velodyne laser scans.
75.76 % 85.40 % 70.29 % 0.03 s GPU @ 2.5 Ghz (Python)
58 PointRGCN 75.73 % 85.97 % 70.60 % 0.26 s GPU @ V100 (Python)
59 IE-PointRCNN 75.67 % 86.26 % 70.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
60 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 75.64 % 86.96 % 70.70 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
61 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 75.43 % 86.10 % 68.88 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
62 PPFNet code 75.43 % 85.91 % 68.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
63 HR-SECOND code 75.32 % 84.78 % 68.70 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
64 NU-optim 75.30 % 85.72 % 69.80 % 0.04 s GPU @ >3.5 Ghz (Python)
65 R-GCN 75.26 % 83.42 % 68.73 % 0.16 s GPU @ 2.5 Ghz (Python)
66 SPA 75.25 % 85.35 % 70.46 % 0.1 s 1 core @ 2.5 Ghz (Python)
67 epBRM
This method makes use of Velodyne laser scans.
code 75.15 % 85.00 % 69.84 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
68 MuRF 75.11 % 84.81 % 69.99 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
69 PBASN code 75.02 % 83.16 % 69.72 % NA s GPU @ 2.5 Ghz (Python)
70 PI-RCNN 74.82 % 84.37 % 70.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
71 PCSC-Net 74.72 % 85.19 % 70.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
72 CentrNet-FG 74.47 % 83.67 % 69.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
73 TBA 74.37 % 83.36 % 69.57 % 0.07 s 1 core @ 2.5 Ghz (Python)
74 RethinkDet3D 74.35 % 82.81 % 67.90 % 0.15 s 1 core @ 2.5 Ghz (Python)
75 MPNet
This method makes use of Velodyne laser scans.
74.34 % 85.42 % 68.59 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
76 PointPillars
This method makes use of Velodyne laser scans.
code 74.31 % 82.58 % 68.99 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
77 Bit 74.30 % 82.67 % 68.73 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
78 Prune 74.28 % 85.03 % 67.16 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
79 autoRUC 74.08 % 84.54 % 67.04 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
80 ARPNET 74.04 % 84.69 % 68.64 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
81 MVSLN 74.00 % 85.19 % 66.81 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
82 PointPiallars_SECA 73.99 % 82.62 % 69.98 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
83 VOXEL_FPN_HR 73.98 % 85.33 % 68.67 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
84 autonet 73.83 % 82.66 % 67.93 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
85 A-VoxelNet 73.82 % 84.01 % 66.46 % 0.029 s GPU @ 2.5 Ghz (Python)
86 PC-CNN-V2
This method makes use of Velodyne laser scans.
73.79 % 85.57 % 65.65 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
87 FOFNet
This method makes use of Velodyne laser scans.
73.70 % 84.56 % 68.09 % 0.04 s GPU @ 2.5 Ghz (Python)
88 C-GCN 73.62 % 83.49 % 67.01 % 0.147 s GPU @ V100 (Python)
89 baseline 73.55 % 82.92 % 67.42 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
90 3DBN
This method makes use of Velodyne laser scans.
73.53 % 83.77 % 66.23 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
91 DDB
This method makes use of Velodyne laser scans.
73.49 % 82.45 % 67.82 % 0.05 s GPU @ 2.5 Ghz (Python)
92 BVVF 73.34 % 80.19 % 67.34 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
93 MP 73.32 % 84.00 % 67.82 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
94 SCNet
This method makes use of Velodyne laser scans.
73.17 % 83.34 % 67.93 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
95 SAANet 73.14 % 84.30 % 66.28 % 0.10 s 1 core @ 2.5 Ghz (Python)
96 SFB-SECOND 73.07 % 83.66 % 68.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
97 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
72.88 % 82.16 % 66.36 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
98 RUC 72.65 % 80.76 % 68.74 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
99 PP-3D 72.20 % 80.35 % 63.54 % 0.1 s 1 core @ 2.5 Ghz (Python)
100 AVOD-FPN
This method makes use of Velodyne laser scans.
code 71.76 % 83.07 % 65.73 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
101 PointPainting
This method makes use of Velodyne laser scans.
71.70 % 82.11 % 67.08 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
102 RUC code 71.40 % 80.98 % 65.98 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
103 RUC code 71.32 % 81.07 % 64.69 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
104 FCY
This method makes use of Velodyne laser scans.
70.78 % 81.48 % 65.30 % 0.02 s GPU @ 2.5 Ghz (Python)
105 WS3D
This method makes use of Velodyne laser scans.
70.59 % 80.99 % 64.23 % 0.1 s GPU @ 2.5 Ghz (Python)
106 PAD 70.33 % 78.94 % 64.83 % 0.15 s 1 core @ 2.5 Ghz (Python)
107 F-PointNet
This method makes use of Velodyne laser scans.
code 69.79 % 82.19 % 60.59 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
108 RuiRUC 69.32 % 81.45 % 57.64 % 0.12 s 1 core @ 2.5 Ghz (Python)
109 UberATG-ContFuse
This method makes use of Velodyne laser scans.
68.78 % 83.68 % 61.67 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
110 SCANet 68.12 % 78.65 % 61.44 % 0.17 s >8 cores @ 2.5 Ghz (Python)
111 RADNet-Fusion
This method makes use of Velodyne laser scans.
68.05 % 79.67 % 63.32 % 0.1 s 1 core @ 2.5 Ghz (Python)
112 RTL3D 67.79 % 80.72 % 61.34 % 0.02 s GPU @ 2.5 Ghz (Python)
113 MLOD
This method makes use of Velodyne laser scans.
code 67.76 % 77.24 % 62.05 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
114 RADNet-LIDAR
This method makes use of Velodyne laser scans.
67.29 % 79.71 % 61.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
115 SECA 66.51 % 79.04 % 60.18 % 1 s GPU @ 2.5 Ghz (Python)
116 AVOD
This method makes use of Velodyne laser scans.
code 66.47 % 76.39 % 60.23 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
117 seivl 66.40 % 77.00 % 63.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
118 Multi-3D
This method makes use of Velodyne laser scans.
66.35 % 78.45 % 55.93 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
119 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).
66.23 % 81.91 % 60.67 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
120 FailNet-Fusion
This method makes use of Velodyne laser scans.
65.07 % 79.50 % 58.86 % 0.1 s 1 core @ 2.5 Ghz (Python)
121 voxelrcnn 64.77 % 73.60 % 60.05 % 15 s 1 core @ 2.5 Ghz (C/C++)
122 3DNN 64.74 % 76.32 % 58.10 % 0.09 s GPU @ 2.5 Ghz (Python)
123 VoxelNet(Unofficial) 64.17 % 77.82 % 57.51 % 0.5 s GPU @ 2.0 Ghz (Python)
124 MV3D
This method makes use of Velodyne laser scans.
63.63 % 74.97 % 54.00 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
125 FailNet-LIDAR
This method makes use of Velodyne laser scans.
62.07 % 76.07 % 55.89 % 0.1 s 1 core @ 2.5 Ghz (Python)
126 ANM 59.07 % 74.99 % 47.64 % 0.12 s 1 core @ 2.5 Ghz (Python)
127 A3DODWTDA
This method makes use of Velodyne laser scans.
code 56.82 % 62.84 % 48.12 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
128 anm 56.17 % 70.34 % 48.11 % 3 s 1 core @ 2.5 Ghz (C/C++)
129 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 54.88 % 68.38 % 49.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
130 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
54.54 % 68.35 % 49.16 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
131 avodC 54.03 % 67.80 % 47.95 % 0.1 s GPU @ 2.5 Ghz (Python)
132 CG-Stereo
This method uses stereo information.
53.58 % 74.39 % 46.50 % 0.57 s GeForce RTX 2080 Ti
133 E-VoxelNet 52.39 % 66.35 % 46.74 % 0.1 s GPU @ 2.5 Ghz (Python)
134 DSGN
This method uses stereo information.
code 52.18 % 73.50 % 45.14 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
135 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
51.92 % 58.88 % 44.59 % 0.5 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
136 BirdNet+
This method makes use of Velodyne laser scans.
code 51.85 % 70.14 % 50.03 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
137 Complexer-YOLO
This method makes use of Velodyne laser scans.
47.34 % 55.93 % 42.60 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
138 Pseudo-LiDAR E2E
This method uses stereo information.
43.92 % 64.75 % 38.14 % 0.4 s GPU @ 2.5 Ghz (Python)
139 Pseudo-LiDAR++
This method uses stereo information.
code 42.43 % 61.11 % 36.99 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
140 Disp R-CNN (velo)
This method uses stereo information.
39.34 % 59.58 % 31.99 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
141 ZoomNet
This method uses stereo information.
code 38.64 % 55.98 % 30.97 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
142 stereo_sa
This method uses stereo information.
37.92 % 58.70 % 31.99 % 0.3 s GPU @ 2.5 Ghz (Python)
143 Disp R-CNN
This method uses stereo information.
37.91 % 58.53 % 31.93 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
144 OC Stereo
This method uses stereo information.
37.60 % 55.15 % 30.25 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
145 Pseudo-Lidar
This method uses stereo information.
code 34.05 % 54.53 % 28.25 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
146 m-prcnn
This method uses stereo information.
31.21 % 53.96 % 24.52 % 0.43 s 1 core @ 2.5 Ghz (Python)
147 Stereo R-CNN
This method uses stereo information.
code 30.23 % 47.58 % 23.72 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
148 IDA-3D
This method uses stereo information.
29.32 % 45.09 % 23.13 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
149 BirdNet
This method makes use of Velodyne laser scans.
27.26 % 40.99 % 25.32 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
150 RT3D-GMP
This method uses stereo information.
23.83 % 32.44 % 17.91 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
151 RT3DStereo
This method uses stereo information.
23.28 % 29.90 % 18.96 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
152 ASOD 22.37 % 38.42 % 17.01 % 0.28 s GPU @ 2.5 Ghz (Python)
153 RT3D
This method makes use of Velodyne laser scans.
19.14 % 23.74 % 18.86 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
154 StereoFENet
This method uses stereo information.
18.41 % 29.14 % 14.20 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
155 SAIC-SA-3D
This method makes use of Velodyne laser scans.
17.79 % 24.78 % 16.56 % 0.05 s GPU @ 2.5 Ghz (Python)
156 PSMD 13.57 % 21.37 % 10.89 % 0.1 s GPU @ 2.5 Ghz (Python)
157 deprecated 13.30 % 14.81 % 11.04 % 1 core @ 2.5 Ghz (C/C++)
158 S3D 12.75 % 14.58 % 10.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
159 DP3D 12.24 % 18.84 % 8.96 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
160 Licar
This method makes use of Velodyne laser scans.
12.10 % 15.23 % 11.39 % 0.09 s GPU @ 2.0 Ghz (Python)
161 D4LCN code 11.72 % 16.65 % 9.51 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
162 DA-3Ddet 11.50 % 16.77 % 8.93 % 0.05 s GPU @ 2.5 Ghz (Python)
163 HG-Mono 11.42 % 16.75 % 8.28 % 0.46 s GPU @ 2.5 Ghz (C/C++)
164 DP3D 11.22 % 17.27 % 8.54 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
165 LNET 11.21 % 12.79 % 9.94 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
166 RefinedMPL 11.14 % 18.09 % 8.94 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
167 PatchNet 11.12 % 15.68 % 10.17 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
168 AM3D 10.74 % 16.50 % 9.52 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
169 YoloMono3D 10.59 % 17.18 % 7.09 % 0.05 s GPU @ 2.5 Ghz (Python)
170 RTM3D code 10.34 % 14.41 % 8.77 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
171 MonoPair 9.99 % 13.04 % 8.65 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
172 SMOKE code 9.76 % 14.03 % 7.84 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
173 M3D-RPN code 9.71 % 14.76 % 7.42 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
174 SS3D_HW 9.70 % 14.74 % 7.22 % 0.4 s GPU @ 2.5 Ghz (Python)
175 MonoSS 9.61 % 13.74 % 7.75 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
176 TopNet-HighRes
This method makes use of Velodyne laser scans.
9.28 % 12.67 % 7.95 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
177 Mono3CN 9.17 % 12.73 % 7.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
178 RAR-Net 8.73 % 13.70 % 6.92 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
179 PG-MonoNet 8.52 % 13.24 % 6.73 % 0.19 s GPU @ 2.5 Ghz (Python)
180 MonoDIS 7.94 % 10.37 % 6.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
181 SS3D 7.68 % 10.78 % 6.51 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
182 Mono3D_PLiDAR code 7.50 % 10.76 % 6.10 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
183 Decoupled-3D v2 7.28 % 11.68 % 5.69 % 0.08 s GPU @ 2.5 Ghz (C/C++)
184 MonoPSR code 7.25 % 10.76 % 5.85 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
185 Decoupled-3D 7.02 % 11.08 % 5.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
186 VoxelJones code 6.35 % 7.39 % 5.80 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
187 RADNet-Mono 5.80 % 8.42 % 4.62 % 0.1 s 1 core @ 2.5 Ghz (Python)
188 MonoGRNet code 5.74 % 9.61 % 4.25 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
189 A3DODWTDA (image) code 5.27 % 6.88 % 4.45 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
190 MonoFENet 5.14 % 8.35 % 4.10 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
191 mylsi-faster-rcnn 5.07 % 8.36 % 4.21 % 0.3 s 1 core @ 2.5 Ghz (Python)
192 OACV 4.77 % 8.13 % 3.78 % 0.23 s GPU @ 2.5 Ghz (Python)
193 mymask-rcnn 4.21 % 8.54 % 3.44 % 0.3 s 1 core @ 2.5 Ghz (Python)
194 FailNet-Mono 4.19 % 6.84 % 3.28 % 0.1 s 1 core @ 2.5 Ghz (Python)
195 CSoR
This method makes use of Velodyne laser scans.
4.06 % 5.61 % 3.17 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
196 Shift R-CNN (mono) code 3.87 % 6.88 % 2.83 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
197 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
198 TopNet-UncEst
This method makes use of Velodyne laser scans.
3.02 % 3.24 % 2.26 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
199 GS3D 2.90 % 4.47 % 2.47 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
200 3D-GCK 2.52 % 3.27 % 2.11 % 24 ms Tesla V100
201 ROI-10D 2.02 % 4.32 % 1.46 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
202 FQNet 1.51 % 2.77 % 1.01 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
203 3D-SSMFCNN code 1.41 % 1.88 % 1.11 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
204 3DVSSD 0.73 % 0.87 % 0.63 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
205 monoref3d 0.04 % 0.08 % 0.04 % 0.1 s 1 core @ 2.5 Ghz (Python)
206 ref3D 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
207 ref3D 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
208 ANM 0.00 % 0.00 % 0.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
209 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 OHS 45.37 % 53.10 % 41.47 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
2 Noah CV Lab - SSL 45.23 % 52.85 % 41.28 % 0.1 s GPU @ 2.5 Ghz (Python)
3 44.81 % 51.29 % 41.13 %
4 TANet code 44.34 % 53.72 % 40.49 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
5 A-VoxelNet 44.30 % 53.66 % 40.43 % 0.029 s GPU @ 2.5 Ghz (Python)
6 3DSSD 44.27 % 54.64 % 40.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
7 PPBA 44.08 % 52.65 % 41.54 % NA s GPU @ 2.5 Ghz (Python)
8 CentrNet-FG 44.02 % 53.51 % 40.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
9 Point-GNN
This method makes use of Velodyne laser scans.
code 43.77 % 51.92 % 40.14 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
10 MVX-Net++ 43.73 % 50.90 % 39.96 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
11 KNN-GCNN 43.57 % 51.82 % 40.02 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
12 F-ConvNet
This method makes use of Velodyne laser scans.
code 43.38 % 52.16 % 38.80 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
13 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 43.35 % 53.10 % 40.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
14 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 43.29 % 52.17 % 40.29 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
15 VMVS
This method makes use of Velodyne laser scans.
43.27 % 53.44 % 39.51 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
16 RethinkDet3D 43.25 % 53.13 % 40.58 % 0.15 s 1 core @ 2.5 Ghz (Python)
17 STD 42.47 % 53.29 % 38.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
18 AVOD-FPN
This method makes use of Velodyne laser scans.
code 42.27 % 50.46 % 39.04 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
19 F-PointNet
This method makes use of Velodyne laser scans.
code 42.15 % 50.53 % 38.08 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
20 PointPillars
This method makes use of Velodyne laser scans.
code 41.92 % 51.45 % 38.89 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
21 epBRM
This method makes use of Velodyne laser scans.
code 41.52 % 49.17 % 39.08 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
22 CentrNet-v1
This method makes use of Velodyne laser scans.
41.50 % 50.86 % 38.24 % 0.03 s GPU @ 2.5 Ghz (Python)
23 TBU 41.16 % 49.33 % 38.84 % NA s GPU @ 2.5 Ghz (Python)
24 PiP 41.01 % 49.01 % 37.90 % 0.05 s 1 core @ 2.5 Ghz (Python)
25 PointPainting
This method makes use of Velodyne laser scans.
40.97 % 50.32 % 37.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
26 Multi-3D
This method makes use of Velodyne laser scans.
40.72 % 49.50 % 36.22 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
27 DDB
This method makes use of Velodyne laser scans.
40.40 % 49.03 % 37.04 % 0.05 s GPU @ 2.5 Ghz (Python)
28 PPFNet code 40.11 % 48.36 % 37.00 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
29 PP-3D 39.76 % 49.59 % 36.49 % 0.1 s 1 core @ 2.5 Ghz (Python)
30 39.72 % 47.14 % 37.25 %
31 LDAM 39.55 % 45.15 % 37.27 % 24 ms GTX 1080 ti GPU
32 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
39.47 % 47.87 % 36.35 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
33 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 39.37 % 47.98 % 36.01 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
34 ARPNET 39.31 % 48.32 % 35.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
35 SCNet
This method makes use of Velodyne laser scans.
38.66 % 47.83 % 35.70 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
36 SCANet 37.93 % 48.41 % 34.10 % 0.17 s >8 cores @ 2.5 Ghz (Python)
37 FOFNet
This method makes use of Velodyne laser scans.
37.56 % 47.45 % 34.00 % 0.04 s GPU @ 2.5 Ghz (Python)
38 MLOD
This method makes use of Velodyne laser scans.
code 37.47 % 47.58 % 35.07 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
39 VOXEL_FPN_HR 37.01 % 46.32 % 34.67 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
40 FCY
This method makes use of Velodyne laser scans.
36.99 % 44.40 % 34.54 % 0.02 s GPU @ 2.5 Ghz (Python)
41 deprecated 36.25 % 47.69 % 32.18 % 0.05 s GPU @ 2.0 Ghz (Python)
42 HR-SECOND code 35.52 % 45.31 % 33.14 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
43 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 34.59 % 42.27 % 31.37 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
44 PBASN code 34.48 % 41.28 % 32.24 % NA s GPU @ 2.5 Ghz (Python)
45 MP 33.89 % 43.04 % 31.46 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
46 anm 32.98 % 43.55 % 29.12 % 3 s 1 core @ 2.5 Ghz (C/C++)
47 BirdNet+
This method makes use of Velodyne laser scans.
code 31.46 % 37.99 % 29.46 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
48 SAANet 30.61 % 38.50 % 27.35 % 0.10 s 1 core @ 2.5 Ghz (Python)
49 SparsePool code 30.38 % 37.84 % 26.94 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
50 SparsePool code 27.92 % 35.52 % 25.87 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
51 AVOD
This method makes use of Velodyne laser scans.
code 27.86 % 36.10 % 25.76 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
52 27.75 % 35.85 % 25.09 %
53 CSW3D
This method makes use of Velodyne laser scans.
26.64 % 33.75 % 23.34 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
54 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
24.84 % 31.61 % 21.96 % 0.5 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
55 CG-Stereo
This method uses stereo information.
24.31 % 33.22 % 20.95 % 0.57 s GeForce RTX 2080 Ti
56 OC Stereo
This method uses stereo information.
17.58 % 24.48 % 15.60 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
57 BirdNet
This method makes use of Velodyne laser scans.
17.08 % 22.04 % 15.82 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
58 DSGN
This method uses stereo information.
code 15.55 % 20.53 % 14.15 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
59 Complexer-YOLO
This method makes use of Velodyne laser scans.
13.96 % 17.60 % 12.70 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
60 RefinedMPL 7.18 % 11.14 % 5.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
61 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.92 % 10.40 % 6.63 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
62 MonoPair 6.68 % 10.02 % 5.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
63 SS3D_HW 5.00 % 7.77 % 4.03 % 0.4 s GPU @ 2.5 Ghz (Python)
64 Shift R-CNN (mono) code 4.66 % 7.95 % 4.16 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
65 MonoPSR code 4.00 % 6.12 % 3.30 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
66 DP3D 3.54 % 4.75 % 2.88 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
67 M3D-RPN code 3.48 % 4.92 % 2.94 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
68 HG-Mono 3.46 % 4.75 % 2.85 % 0.46 s GPU @ 2.5 Ghz (C/C++)
69 Mono3CN 3.44 % 5.13 % 3.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 RT3D-GMP
This method uses stereo information.
3.42 % 4.51 % 2.77 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
71 D4LCN code 3.42 % 4.55 % 2.83 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
72 DP3D 3.37 % 4.77 % 2.77 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
73 PG-MonoNet 2.58 % 3.61 % 2.36 % 0.19 s GPU @ 2.5 Ghz (Python)
74 RT3DStereo
This method uses stereo information.
2.45 % 3.28 % 2.35 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
75 TopNet-UncEst
This method makes use of Velodyne laser scans.
1.87 % 3.42 % 1.73 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
76 SS3D 1.78 % 2.31 % 1.48 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
77 mylsi-faster-rcnn 0.96 % 1.36 % 0.62 % 0.3 s 1 core @ 2.5 Ghz (Python)
78 mymask-rcnn 0.80 % 1.16 % 0.71 % 0.3 s 1 core @ 2.5 Ghz (Python)
79 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 71.53 % 84.24 % 62.20 % 0.1 s GPU @ 2.5 Ghz (Python)
2 OHS 65.95 % 82.59 % 59.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
3 F-ConvNet
This method makes use of Velodyne laser scans.
code 65.07 % 81.98 % 56.54 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
4 3DSSD 64.10 % 82.48 % 56.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
5 PointPainting
This method makes use of Velodyne laser scans.
63.78 % 77.63 % 55.89 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
6 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 63.71 % 78.60 % 57.65 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
7 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 63.52 % 79.17 % 56.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
8 Point-GNN
This method makes use of Velodyne laser scans.
code 63.48 % 78.60 % 57.08 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
9 63.16 % 77.70 % 57.16 %
10 KNN-GCNN 62.91 % 80.24 % 56.49 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
11 62.72 % 79.09 % 56.76 %
12 PPBA 61.99 % 79.42 % 55.34 % NA s GPU @ 2.5 Ghz (Python)
13 TBU 61.99 % 79.42 % 55.34 % NA s GPU @ 2.5 Ghz (Python)
14 VOXEL_FPN_HR 61.91 % 78.29 % 55.54 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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15 STD 61.59 % 78.69 % 55.30 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
16 RethinkDet3D 61.10 % 79.31 % 54.47 % 0.15 s 1 core @ 2.5 Ghz (Python)
17 MVX-Net++ 61.03 % 76.07 % 53.41 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
18 HR-SECOND code 60.82 % 75.83 % 53.67 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
19 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 60.30 % 75.42 % 53.81 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
20 FOFNet
This method makes use of Velodyne laser scans.
59.73 % 76.23 % 53.44 % 0.04 s GPU @ 2.5 Ghz (Python)
21 PiP 59.54 % 75.43 % 53.37 % 0.05 s 1 core @ 2.5 Ghz (Python)
22 TANet code 59.44 % 75.70 % 52.53 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
23 PBASN code 59.43 % 76.80 % 52.77 % NA s GPU @ 2.5 Ghz (Python)
24 Multi-3D
This method makes use of Velodyne laser scans.
59.04 % 76.77 % 50.45 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
25 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 58.82 % 74.96 % 52.53 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
26 PointPillars
This method makes use of Velodyne laser scans.
code 58.65 % 77.10 % 51.92 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
27 ARPNET 58.20 % 74.21 % 52.13 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
28 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
57.15 % 73.69 % 50.17 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
29 LDAM 56.79 % 71.66 % 50.82 % 24 ms GTX 1080 ti GPU
30 epBRM
This method makes use of Velodyne laser scans.
code 56.13 % 72.08 % 49.91 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
31 F-PointNet
This method makes use of Velodyne laser scans.
code 56.12 % 72.27 % 49.01 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
32 A-VoxelNet 55.86 % 72.58 % 49.13 % 0.029 s GPU @ 2.5 Ghz (Python)
33 deprecated 55.58 % 77.86 % 48.49 % 0.05 s GPU @ 2.0 Ghz (Python)
34 CentrNet-FG 55.54 % 72.07 % 49.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
35 MP 55.36 % 72.99 % 49.36 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
36 FCY
This method makes use of Velodyne laser scans.
54.91 % 73.11 % 48.16 % 0.02 s GPU @ 2.5 Ghz (Python)
37 CentrNet-v1
This method makes use of Velodyne laser scans.
54.64 % 72.03 % 48.03 % 0.03 s GPU @ 2.5 Ghz (Python)
38 SCANet 53.38 % 68.71 % 47.59 % 0.17 s >8 cores @ 2.5 Ghz (Python)
39 DDB
This method makes use of Velodyne laser scans.
51.38 % 68.83 % 45.15 % 0.05 s GPU @ 2.5 Ghz (Python)
40 SCNet
This method makes use of Velodyne laser scans.
50.79 % 67.98 % 45.15 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
41 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.55 % 63.76 % 44.93 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
42 MLOD
This method makes use of Velodyne laser scans.
code 49.43 % 68.81 % 42.84 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
43 PP-3D 49.19 % 66.54 % 42.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
44 SAANet 48.67 % 62.76 % 43.45 % 0.10 s 1 core @ 2.5 Ghz (Python)
45 BirdNet+
This method makes use of Velodyne laser scans.
code 47.72 % 67.38 % 42.89 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
46 AVOD
This method makes use of Velodyne laser scans.
code 42.08 % 57.19 % 38.29 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
47 SparsePool code 37.33 % 52.61 % 33.39 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
48 anm 33.28 % 49.27 % 28.90 % 3 s 1 core @ 2.5 Ghz (C/C++)
49 SparsePool code 32.61 % 40.87 % 29.05 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
50 CG-Stereo
This method uses stereo information.
30.89 % 47.40 % 27.23 % 0.57 s GeForce RTX 2080 Ti
51 BirdNet
This method makes use of Velodyne laser scans.
30.25 % 43.98 % 27.21 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
52 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.53 % 24.27 % 17.31 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
53 DSGN
This method uses stereo information.
code 18.17 % 27.76 % 16.21 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
54 OC Stereo
This method uses stereo information.
16.63 % 29.40 % 14.72 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
55 RT3D-GMP
This method uses stereo information.
4.90 % 7.75 % 4.18 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
56 MonoPSR code 4.74 % 8.37 % 3.68 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
57 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.54 % 7.13 % 3.81 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
58 RT3DStereo
This method uses stereo information.
3.37 % 5.29 % 2.57 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
59 Mono3CN 2.17 % 3.68 % 2.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
60 SS3D_HW 2.17 % 4.29 % 2.00 % 0.4 s GPU @ 2.5 Ghz (Python)
61 MonoPair 2.12 % 3.79 % 1.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
62 RefinedMPL 1.82 % 3.23 % 1.77 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
63 TopNet-HighRes
This method makes use of Velodyne laser scans.
1.67 % 2.49 % 1.88 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
64 D4LCN code 1.67 % 2.45 % 1.36 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
65 DP3D 1.66 % 2.77 % 1.31 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
66 SS3D 1.45 % 2.80 % 1.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
67 DP3D 1.39 % 2.04 % 1.12 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
68 HG-Mono 1.12 % 2.01 % 1.19 % 0.46 s GPU @ 2.5 Ghz (C/C++)
69 mylsi-faster-rcnn 1.07 % 1.71 % 0.85 % 0.3 s 1 core @ 2.5 Ghz (Python)
70 PG-MonoNet 0.90 % 1.59 % 0.95 % 0.19 s GPU @ 2.5 Ghz (Python)
71 M3D-RPN code 0.65 % 0.94 % 0.47 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
72 mymask-rcnn 0.30 % 0.71 % 0.28 % 0.3 s 1 core @ 2.5 Ghz (Python)
73 Shift R-CNN (mono) code 0.29 % 0.48 % 0.31 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
74 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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