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 PV-RCNN
This method makes use of Velodyne laser scans.
81.43 % 90.25 % 76.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
2 SA-SSD 79.79 % 88.75 % 74.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
3 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.
4 3DSSD 79.57 % 88.36 % 74.55 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
5 Point-GNN
This method makes use of Velodyne laser scans.
79.47 % 88.33 % 72.29 % 0.6 s GPU @ 2.5 Ghz (Python)
6 EPNet 79.28 % 89.81 % 74.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
7 CPRCCNN 78.96 % 87.74 % 74.30 % 0.1 s 1 core @ 2.5 Ghz (Python)
8 ORP 78.50 % 87.38 % 71.49 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
9 MMLab-PartA^2
This method makes use of Velodyne laser scans.
78.49 % 87.81 % 73.51 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, X. Wang and H. Li: Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. arXiv preprint arXiv:1907.03670 2019.
10 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.
11 ELE 78.35 % 86.95 % 73.33 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
12 OHS 78.34 % 88.12 % 73.49 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
13 HRI-FusionRCNN 78.29 % 88.46 % 70.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
14 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++)
15 MLF_SecCas 77.97 % 86.53 % 67.90 % 0.05 s 1 core @ 2.5 Ghz (Python)
16 OHS + Occ 77.74 % 86.40 % 72.97 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
17 Noah CV Lab - SSL 77.73 % 85.91 % 70.88 % 0.1 s GPU @ 2.5 Ghz (Python)
18 deprecated 77.62 % 86.21 % 67.68 % 0.05 s GPU @ 2.0 Ghz (Python)
19 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.
20 Associate-3Ddet
This method makes use of Velodyne laser scans.
77.40 % 85.99 % 70.53 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
21 Fast Point R-CNN v1
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.
22 3D-CVF
This method makes use of Velodyne laser scans.
77.31 % 86.44 % 70.91 % 0.05 s GPU @ >3.5 Ghz (Python)
23 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.
24 deprecated 77.17 % 86.27 % 70.83 % 0.05 s GPU @ >3.5 Ghz (Python)
25 DEFT 77.15 % 86.34 % 70.76 % 1 s GPU @ 2.5 Ghz (Python)
26 HRI-VoxelFPN 76.70 % 85.64 % 69.44 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
B. Wang, J. An and J. Cao: Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds. arXiv preprint arXiv:1907.05286v2 2019.
27 MLF_PointCas 76.68 % 87.50 % 71.21 % 0.1 s GPU @ 2.5 Ghz (Python)
28 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.
29 SRF 76.61 % 86.63 % 71.28 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
30 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.
31 F-ConvNet
This method makes use of Velodyne laser scans.
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.
32 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++)
33 RGB3D
This method makes use of Velodyne laser scans.
76.26 % 87.26 % 71.16 % 0.39 s GPU @ 2.5 Ghz (Python)
34 PiP 76.24 % 85.30 % 70.45 % 0.05 s 1 core @ 2.5 Ghz (Python)
35 SegVoxelNet 76.13 % 86.04 % 70.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
36 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++)
37 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++)
38 TANet 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.
39 MMV 75.91 % 84.46 % 68.69 % 0.4 s GPU @ 2.5 Ghz (C/C++)
40 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++)
41 CentrNet-v1
This method makes use of Velodyne laser scans.
75.76 % 85.40 % 70.29 % 0.03 s GPU @ 2.5 Ghz (Python)
42 PointRGCN 75.73 % 85.97 % 70.60 % 0.26 s GPU @ V100 (Python)
43 IE-PointRCNN 75.67 % 86.26 % 70.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
44 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.
45 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.
46 PPFNet code 75.43 % 85.91 % 68.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
47 HR-SECOND code 75.32 % 84.78 % 68.70 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
48 NU-optim 75.30 % 85.72 % 69.80 % 0.04 s GPU @ >3.5 Ghz (Python)
49 R-GCN 75.26 % 83.42 % 68.73 % 0.16 s GPU @ 2.5 Ghz (Python)
50 SPA 75.25 % 85.35 % 70.46 % 0.1 s 1 core @ 2.5 Ghz (Python)
51 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.
52 DH-ARI 74.88 % 82.12 % 68.76 % 0.2 s 1 core @ >3.5 Ghz (Python + C/C++)
53 PI-RCNN 74.82 % 84.37 % 70.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
54 Fast Point R-CNN
This method makes use of Velodyne laser scans.
74.59 % 84.80 % 67.27 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
55 PFPN 74.52 % 85.30 % 67.21 % 0.02 s 4 cores @ >3.5 Ghz (Python)
56 TBA 74.37 % 83.36 % 69.57 % 0.07 s 1 core @ 2.5 Ghz (Python)
57 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++)
58 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.
59 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
74.10 % 84.61 % 67.03 % 0.2 s GPU @ >3.5 Ghz (Python)
60 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.
61 PCSC-Net 74.03 % 83.18 % 68.39 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
62 MVSLN 74.00 % 85.19 % 66.81 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
63 PointPiallars_SECA 73.99 % 82.62 % 69.98 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
64 VOXEL_FPN_HR 73.98 % 85.33 % 68.67 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
65 A-VoxelNet 73.82 % 84.01 % 66.46 % 0.029 s GPU @ 2.5 Ghz (Python)
66 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.
67 FOFNet
This method makes use of Velodyne laser scans.
73.70 % 84.56 % 68.09 % 0.04 s GPU @ 2.5 Ghz (Python)
68 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.
69 DDB
This method makes use of Velodyne laser scans.
73.49 % 82.45 % 67.82 % 0.05 s GPU @ 2.5 Ghz (Python)
70 CFR
This method makes use of Velodyne laser scans.
73.35 % 84.42 % 66.02 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
71 BVVF 73.34 % 80.19 % 67.34 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
72 MP 73.32 % 84.00 % 67.82 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
73 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.
74 SAANet 73.14 % 84.30 % 66.28 % 0.10 s 1 core @ 2.5 Ghz (Python)
75 SFB-SECOND 73.07 % 83.66 % 68.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
76 IPOD 73.04 % 80.30 % 68.73 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
77 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++)
78 RUC 72.65 % 80.76 % 68.74 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
79 AILabs3D
This method makes use of Velodyne laser scans.
72.63 % 83.85 % 63.75 % 0.6 s GPU @ >3.5 Ghz (Python)
80 SECOND code 72.55 % 83.34 % 65.82 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
81 MVX-Net
This method makes use of Velodyne laser scans.
71.95 % 84.99 % 64.88 % 0.06 s GPU @ 3.0 Ghz (Python + C/C++)
82 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.
83 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. arXiv preprint arXiv:1911.10150 2019.
84 MDC
This method makes use of Velodyne laser scans.
71.47 % 82.66 % 62.27 % 0.17 s GPU @ 2.5 Ghz (Python)
85 PP_v1.0 code 70.34 % 80.15 % 64.58 % 0.02s 1 core @ 2.5 Ghz (C/C++)
86 PAD 70.33 % 78.94 % 64.83 % 0.15 s 1 core @ 2.5 Ghz (Python)
87 CONV-BOX
This method makes use of Velodyne laser scans.
70.03 % 80.98 % 65.66 % 0.2 s Tesla V100
88 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.
89 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
69.39 % 79.27 % 64.41 % 0.035 s GPU (C++)
90 RuiRUC 69.32 % 81.45 % 57.64 % 0.12 s 1 core @ 2.5 Ghz (Python)
91 DFD 69.20 % 79.84 % 62.32 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
92 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.
93 SCANet 68.12 % 78.65 % 61.44 % 0.17 s >8 cores @ 2.5 Ghz (Python)
94 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)
95 ELLIOT
This method makes use of Velodyne laser scans.
67.96 % 79.06 % 63.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
96 RTL3D 67.79 % 80.72 % 61.34 % 0.02 s GPU @ 2.5 Ghz (Python)
97 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.
98 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)
99 SCANet 67.13 % 79.22 % 60.65 % 0.09s GPU @ 2.5 Ghz (Python)
100 SECA 66.51 % 79.04 % 60.18 % 1 s GPU @ 2.5 Ghz (Python)
101 VSE 66.51 % 79.04 % 60.18 % 0.15 s GPU @ 2.5 Ghz (Python)
102 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.
103 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++)
104 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++)
105 FNV1_RPN 65.99 % 77.98 % 57.99 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
106 X_MD 65.89 % 77.52 % 59.79 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
107 SECA 65.53 % 76.87 % 59.18 % 0.09 s GPU @ 2.5 Ghz (Python)
108 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)
109 3DNN 64.74 % 76.32 % 58.10 % 0.09 s GPU @ 2.5 Ghz (Python)
110 NLK 64.49 % 76.78 % 59.37 % 0.02 s 1 core @ 2.5 Ghz (Python)
111 FNV1_Fusion 64.21 % 76.27 % 57.65 % 0.11 s GPU @ 2.5 Ghz (Python)
112 VoxelNet(Unofficial) 64.17 % 77.82 % 57.51 % 0.5 s GPU @ 2.0 Ghz (Python)
113 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.
114 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)
115 FNV2 60.35 % 69.39 % 50.96 % 0.18 s GPU @ 2.5 Ghz (Python)
116 FNV1 60.24 % 71.81 % 53.91 % 0.11 s GPU @ 2.5 Ghz (Python)
117 ANM 59.07 % 74.99 % 47.64 % 0.12 s 1 core @ 2.5 Ghz (Python)
118 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.
119 anm 56.17 % 70.34 % 48.11 % 3 s 1 core @ 2.5 Ghz (C/C++)
120 CLF3D
This method makes use of Velodyne laser scans.
55.94 % 67.04 % 46.79 % 0.13 s GPU @ 2.5 Ghz (Python)
121 PL V2 (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
54.88 % 68.38 % 49.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
122 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.
123 avodC 54.03 % 67.80 % 47.95 % 0.1 s GPU @ 2.5 Ghz (Python)
124 E-VoxelNet 52.39 % 66.35 % 46.74 % 0.1 s GPU @ 2.5 Ghz (Python)
125 DSGN
This method uses stereo information.
52.18 % 73.50 % 45.14 % 0.67 s NVIDIA Tesla V100
126 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.
127 Pseudo-LiDAR E2E
This method uses stereo information.
43.92 % 64.75 % 38.14 % 0.4 s GPU @ 2.5 Ghz (Python)
128 Pseudo-LiDAR V2
This method uses stereo information.
code 42.43 % 61.11 % 36.99 % 0.4 s GPU @ 2.5 Ghz (Python)
129 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++)
130 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.
131 stereo_sa
This method uses stereo information.
37.92 % 58.70 % 31.99 % 0.3 s GPU @ 2.5 Ghz (Python)
132 Disp R-CNN
This method uses stereo information.
37.91 % 58.53 % 31.93 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
133 OC Stereo
This method uses stereo information.
37.60 % 55.15 % 30.25 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
134 Pseudo-LiDAR
This method uses stereo information.
code 34.05 % 54.53 % 28.25 % 0.4 s GPU @ 2.5 Ghz (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. CVPR 2019.
135 m-prcnn
This method uses stereo information.
31.21 % 53.96 % 24.52 % 0.43 s 1 core @ 2.5 Ghz (Python)
136 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.
137 30.06 % 48.89 % 24.70 %
138 SA_3D 29.61 % 40.77 % 23.86 % 0.3 s GPU @ 2.5 Ghz (Python)
139 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.
140 ASOD 22.37 % 38.42 % 17.01 % 0.28 s GPU @ 2.5 Ghz (Python)
141 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.
142 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.
143 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)
144 FCY
This method makes use of Velodyne laser scans.
16.74 % 21.47 % 16.76 % 0.02 s GPU @ 2.5 Ghz (Python)
145 DLMB
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
12.50 % 15.26 % 11.14 % 0.03 s 8 cores @ 3.5 Ghz (C/C++)
146 Licar
This method makes use of Velodyne laser scans.
12.10 % 15.23 % 11.39 % 0.09 s GPU @ 2.0 Ghz (Python)
147 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. arXiv preprint arXiv:1912.04799 2019.
148 RefinedMPL 11.14 % 18.09 % 8.94 % 0.1 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.
149 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.
150 MonoPair 9.99 % 13.04 % 8.65 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
151 SMOKE 9.76 % 14.03 % 7.84 % 0.03 s GPU @ 2.5 Ghz (Python)
152 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 .
153 SS3D_HW 9.70 % 14.74 % 7.22 % 0.4 s GPU @ 2.5 Ghz (Python)
154 MonoSS 9.61 % 13.74 % 7.75 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
155 BirdNet
This method makes use of Velodyne laser scans.
9.47 % 13.53 % 8.49 % 0.11 s Titan Xp GPU
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.
156 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.
157 RAR-Net 8.95 % 14.12 % 7.19 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
158 PG-MonoNet 8.52 % 13.24 % 6.73 % 0.19 s GPU @ 2.5 Ghz (Python)
159 DT3D 8.51 % 13.94 % 7.10 % 0,21s GPU @ 2.5 Ghz (Python)
160 MonoDIS 7.94 % 10.37 % 6.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
161 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.
162 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.
163 Decoupled-3D v2 7.28 % 11.68 % 5.69 % 0.08 s GPU @ 2.5 Ghz (C/C++)
164 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.
165 Decoupled-3D 7.02 % 11.08 % 5.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
166 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.
167 RADNet-Mono 5.80 % 8.42 % 4.62 % 0.1 s 1 core @ 2.5 Ghz (Python)
168 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.
169 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.
170 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.
171 mylsi-faster-rcnn 5.07 % 8.36 % 4.21 % 0.3 s 1 core @ 2.5 Ghz (Python)
172 OACV 4.77 % 8.13 % 3.78 % 0.23 s GPU @ 2.5 Ghz (Python)
173 mymask-rcnn 4.21 % 8.54 % 3.44 % 0.3 s 1 core @ 2.5 Ghz (Python)
174 FailNet-Mono 4.19 % 6.84 % 3.28 % 0.1 s 1 core @ 2.5 Ghz (Python)
175 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.
176 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.
177 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 0.18 s GPU @ 2.5 Ghz (Python)
178 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.
179 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.
180 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.
181 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.
182 MF3D 1.48 % 2.86 % 1.27 % 0.03 s GPU @ 2.5 Ghz (C/C++)
183 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.
184 OFT-Net 1.32 % 1.61 % 1.00 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
185 3DVSSD 0.73 % 0.87 % 0.63 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
186 monoref3d 0.04 % 0.08 % 0.04 % 0.1 s 1 core @ 2.5 Ghz (Python)
187 ref3D 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
188 ref3D 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
189 ANM 0.00 % 0.00 % 0.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
190 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 + Occ 44.81 % 51.29 % 41.13 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
2 IPOD 44.37 % 55.07 % 40.05 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
3 TANet 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.
4 Noah CV Lab - SSL 44.34 % 51.22 % 39.03 % 0.1 s GPU @ 2.5 Ghz (Python)
5 A-VoxelNet 44.30 % 53.66 % 40.43 % 0.029 s GPU @ 2.5 Ghz (Python)
6 Point-GNN
This method makes use of Velodyne laser scans.
43.77 % 51.92 % 40.14 % 0.6 s GPU @ 2.5 Ghz (Python)
7 F-ConvNet
This method makes use of Velodyne laser scans.
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.
8 MMLab-PartA^2
This method makes use of Velodyne laser scans.
43.35 % 53.10 % 40.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, X. Wang and H. Li: Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. arXiv preprint arXiv:1907.03670 2019.
9 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.
10 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.
11 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.
12 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.
13 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.
14 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.
15 CentrNet-v1
This method makes use of Velodyne laser scans.
41.50 % 50.86 % 38.24 % 0.03 s GPU @ 2.5 Ghz (Python)
16 PiP 41.01 % 49.01 % 37.90 % 0.05 s 1 core @ 2.5 Ghz (Python)
17 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. arXiv preprint arXiv:1911.10150 2019.
18 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++)
19 DDB
This method makes use of Velodyne laser scans.
40.40 % 49.03 % 37.04 % 0.05 s GPU @ 2.5 Ghz (Python)
20 PPFNet code 40.11 % 48.36 % 37.00 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
21 MDC
This method makes use of Velodyne laser scans.
39.84 % 50.05 % 35.81 % 0.17 s GPU @ 2.5 Ghz (Python)
22 OHS 39.72 % 47.14 % 37.25 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
23 LDAM 39.55 % 45.15 % 37.27 % 0.05 s GPU @ 2.5 Ghz (C/C++)
24 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++)
25 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.
26 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.
27 SECOND code 38.78 % 48.96 % 34.91 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
28 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.
29 CONV-BOX
This method makes use of Velodyne laser scans.
38.12 % 45.69 % 34.55 % 0.2 s Tesla V100
30 SCANet 37.93 % 48.41 % 34.10 % 0.17 s >8 cores @ 2.5 Ghz (Python)
31 PP_v1.0 code 37.57 % 45.93 % 34.66 % 0.02s 1 core @ 2.5 Ghz (C/C++)
32 FOFNet
This method makes use of Velodyne laser scans.
37.56 % 47.45 % 34.00 % 0.04 s GPU @ 2.5 Ghz (Python)
33 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.
34 VOXEL_FPN_HR 37.01 % 46.32 % 34.67 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
35 deprecated 36.25 % 47.69 % 32.18 % 0.05 s GPU @ 2.0 Ghz (Python)
36 HR-SECOND code 35.52 % 45.31 % 33.14 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
37 ELLIOT
This method makes use of Velodyne laser scans.
34.96 % 44.13 % 31.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 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.
39 CFR
This method makes use of Velodyne laser scans.
34.58 % 44.46 % 30.86 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
40 MP 33.89 % 43.04 % 31.46 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
41 anm 32.98 % 43.55 % 29.12 % 3 s 1 core @ 2.5 Ghz (C/C++)
42 SAANet 30.61 % 38.50 % 27.35 % 0.10 s 1 core @ 2.5 Ghz (Python)
43 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.
44 X_MD 29.25 % 38.42 % 25.77 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
45 anonymous
This method makes use of Velodyne laser scans.
28.35 % 36.94 % 24.99 % 0.75 s GPU @ 3.5 Ghz (C/C++)
46 SA_3D 28.04 % 36.11 % 24.81 % 0.3 s GPU @ 2.5 Ghz (Python)
47 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.
48 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.
49 27.75 % 35.85 % 25.09 %
50 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.
51 CLF3D
This method makes use of Velodyne laser scans.
26.40 % 34.94 % 23.14 % 0.13 s GPU @ 2.5 Ghz (Python)
52 OC Stereo
This method uses stereo information.
17.58 % 24.48 % 15.60 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
53 DSGN
This method uses stereo information.
15.55 % 20.53 % 14.15 % 0.67 s NVIDIA Tesla V100
54 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.
55 BirdNet
This method makes use of Velodyne laser scans.
8.99 % 12.25 % 8.06 % 0.11 s Titan Xp GPU
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.
56 FCY
This method makes use of Velodyne laser scans.
8.34 % 11.38 % 7.48 % 0.02 s GPU @ 2.5 Ghz (Python)
57 RefinedMPL 7.18 % 11.14 % 5.84 % 0.1 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.
58 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.
59 MonoPair 6.68 % 10.02 % 5.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
60 SS3D_HW 5.00 % 7.77 % 4.03 % 0.4 s GPU @ 2.5 Ghz (Python)
61 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.
62 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.
63 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 .
64 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. arXiv preprint arXiv:1912.04799 2019.
65 PG-MonoNet 2.58 % 3.61 % 2.36 % 0.19 s GPU @ 2.5 Ghz (Python)
66 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.
67 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.
68 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.
69 mylsi-faster-rcnn 0.96 % 1.36 % 0.62 % 0.3 s 1 core @ 2.5 Ghz (Python)
70 mymask-rcnn 0.80 % 1.16 % 0.71 % 0.3 s 1 core @ 2.5 Ghz (Python)
71 DT3D 0.37 % 0.57 % 0.35 % 0,21s GPU @ 2.5 Ghz (Python)
72 OFT-Net 0.36 % 0.63 % 0.35 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
73 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 70.35 % 83.40 % 61.17 % 0.1 s GPU @ 2.5 Ghz (Python)
2 F-ConvNet
This method makes use of Velodyne laser scans.
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.
3 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. arXiv preprint arXiv:1911.10150 2019.
4 PV-RCNN
This method makes use of Velodyne laser scans.
63.71 % 78.60 % 57.65 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
5 MMLab-PartA^2
This method makes use of Velodyne laser scans.
63.52 % 79.17 % 56.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, X. Wang and H. Li: Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. arXiv preprint arXiv:1907.03670 2019.
6 Point-GNN
This method makes use of Velodyne laser scans.
63.48 % 78.60 % 57.08 % 0.6 s GPU @ 2.5 Ghz (Python)
7 OHS + Occ 63.16 % 77.70 % 57.16 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
8 OHS 62.72 % 79.09 % 56.76 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
9 VOXEL_FPN_HR 61.91 % 78.29 % 55.54 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
10 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.
11 HR-SECOND code 60.82 % 75.83 % 53.67 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
12 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.
13 FOFNet
This method makes use of Velodyne laser scans.
59.73 % 76.23 % 53.44 % 0.04 s GPU @ 2.5 Ghz (Python)
14 PiP 59.54 % 75.43 % 53.37 % 0.05 s 1 core @ 2.5 Ghz (Python)
15 TANet 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.
16 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++)
17 MDC
This method makes use of Velodyne laser scans.
59.02 % 75.54 % 50.56 % 0.17 s GPU @ 2.5 Ghz (Python)
18 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.
19 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.
20 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.
21 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++)
22 LDAM 56.79 % 71.66 % 50.82 % 0.05 s GPU @ 2.5 Ghz (C/C++)
23 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.
24 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.
25 A-VoxelNet 55.86 % 72.58 % 49.13 % 0.029 s GPU @ 2.5 Ghz (Python)
26 deprecated 55.58 % 77.86 % 48.49 % 0.05 s GPU @ 2.0 Ghz (Python)
27 MP 55.36 % 72.99 % 49.36 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
28 CONV-BOX
This method makes use of Velodyne laser scans.
55.27 % 67.27 % 49.33 % 0.2 s Tesla V100
29 CentrNet-v1
This method makes use of Velodyne laser scans.
54.64 % 72.03 % 48.03 % 0.03 s GPU @ 2.5 Ghz (Python)
30 SCANet 53.38 % 68.71 % 47.59 % 0.17 s >8 cores @ 2.5 Ghz (Python)
31 IPOD 52.23 % 71.99 % 46.50 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
32 SECOND code 52.08 % 71.33 % 45.83 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
33 DDB
This method makes use of Velodyne laser scans.
51.38 % 68.83 % 45.15 % 0.05 s GPU @ 2.5 Ghz (Python)
34 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.
35 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.
36 ELLIOT
This method makes use of Velodyne laser scans.
50.14 % 69.37 % 44.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 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.
38 PP_v1.0 code 48.86 % 66.46 % 42.59 % 0.02s 1 core @ 2.5 Ghz (C/C++)
39 SAANet 48.67 % 62.76 % 43.45 % 0.10 s 1 core @ 2.5 Ghz (Python)
40 CFR
This method makes use of Velodyne laser scans.
47.64 % 63.85 % 41.98 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
41 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.
42 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.
43 X_MD 36.24 % 50.58 % 32.70 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
44 anm 33.28 % 49.27 % 28.90 % 3 s 1 core @ 2.5 Ghz (C/C++)
45 CLF3D
This method makes use of Velodyne laser scans.
32.79 % 49.38 % 28.74 % 0.13 s GPU @ 2.5 Ghz (Python)
46 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.
47 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.
48 DSGN
This method uses stereo information.
18.17 % 27.76 % 16.21 % 0.67 s NVIDIA Tesla V100
49 OC Stereo
This method uses stereo information.
16.63 % 29.40 % 14.72 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
50 FCY
This method makes use of Velodyne laser scans.
15.26 % 23.03 % 14.54 % 0.02 s GPU @ 2.5 Ghz (Python)
51 SA_3D 10.68 % 15.18 % 8.83 % 0.3 s GPU @ 2.5 Ghz (Python)
52 BirdNet
This method makes use of Velodyne laser scans.
10.46 % 16.63 % 9.53 % 0.11 s Titan Xp GPU
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.
53 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.
54 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.
55 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.
56 SS3D_HW 2.17 % 4.29 % 2.00 % 0.4 s GPU @ 2.5 Ghz (Python)
57 MonoPair 2.12 % 3.79 % 1.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
58 RefinedMPL 1.82 % 3.23 % 1.77 % 0.1 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.
59 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.
60 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. arXiv preprint arXiv:1912.04799 2019.
61 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.
62 mylsi-faster-rcnn 1.07 % 1.71 % 0.85 % 0.3 s 1 core @ 2.5 Ghz (Python)
63 PG-MonoNet 0.90 % 1.59 % 0.95 % 0.19 s GPU @ 2.5 Ghz (Python)
64 DT3D 0.69 % 0.97 % 0.73 % 0,21s GPU @ 2.5 Ghz (Python)
65 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 .
66 mymask-rcnn 0.30 % 0.71 % 0.28 % 0.3 s 1 core @ 2.5 Ghz (Python)
67 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.
68 OFT-Net 0.06 % 0.14 % 0.07 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
69 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Related Datasets

Citation

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



eXTReMe Tracker