Andreas Geiger (MPI Tübingen) | Philip Lenz (KIT) | Christoph Stiller (KIT) | Raquel Urtasun (University of Toronto)

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 Noah CV Lab - SSL 77.73 % 85.91 % 70.88 % 0.1 s GPU @ 2.5 Ghz (Python)
17 deprecated 77.62 % 86.21 % 67.68 % 0.05 s GPU @ 2.0 Ghz (Python)
18 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.
19 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++)
20 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.
21 3D-CVF
This method makes use of Velodyne laser scans.
 77.31 % 86.44 % 70.91 % 0.05 s GPU @ >3.5 Ghz (Python)
22 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.
23 deprecated 77.17 % 86.27 % 70.83 % 0.05 s GPU @ >3.5 Ghz (Python)
24 DEFT 77.15 % 86.34 % 70.76 % 1 s GPU @ 2.5 Ghz (Python)
25 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.
26 MLF_PointCas 76.68 % 87.50 % 71.21 % 0.1 s GPU @ 2.5 Ghz (Python)
27 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.
28 SRF 76.61 % 86.63 % 71.28 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
29 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.
30 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.
31 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++)
32 RGB3D
This method makes use of Velodyne laser scans.
 76.26 % 87.26 % 71.16 % 0.39 s GPU @ 2.5 Ghz (Python)
33 PiP 76.24 % 85.30 % 70.45 % 0.05 s 1 core @ 2.5 Ghz (Python)
34 SegVoxelNet 76.13 % 86.04 % 70.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
35 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++)
36 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++)
37 TANet 75.94 % 84.39 % 68.82 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
38 MMV 75.91 % 84.46 % 68.69 % 0.4 s GPU @ 2.5 Ghz (C/C++)
39 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++)
40 CentrNet-v1
This method makes use of Velodyne laser scans.
 75.76 % 85.40 % 70.29 % 0.03 s GPU @ 2.5 Ghz (Python)
41 IE-PointRCNN 75.67 % 86.26 % 70.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 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.
43 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.
44 PPFNet code 75.43 % 85.91 % 68.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
45 HR-SECOND code 75.32 % 84.78 % 68.70 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
46 NU-optim 75.30 % 85.72 % 69.80 % 0.04 s GPU @ >3.5 Ghz (Python)
47 SPA 75.25 % 85.35 % 70.46 % 0.1 s 1 core @ 2.5 Ghz (Python)
48 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.
49 DH-ARI 74.88 % 82.12 % 68.76 % 0.2 s 1 core @ >3.5 Ghz (Python + C/C++)
50 PI-RCNN 74.82 % 84.37 % 70.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
51 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++)
52 PFPN 74.52 % 85.30 % 67.21 % 0.02 s 4 cores @ >3.5 Ghz (Python)
53 TBA 74.37 % 83.36 % 69.57 % 0.07 s 1 core @ 2.5 Ghz (Python)
54 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++)
55 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.
56 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
 74.10 % 84.61 % 67.03 % 0.2 s GPU @ >3.5 Ghz (Python)
57 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.
58 PCSC-Net 74.03 % 83.18 % 68.39 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
59 MVSLN 74.00 % 85.19 % 66.81 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
60 VOXEL_FPN_HR 73.98 % 85.33 % 68.67 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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61 A-VoxelNet 73.82 % 84.01 % 66.46 % 0.029 s GPU @ 2.5 Ghz (Python)
62 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.
63 FOFNet
This method makes use of Velodyne laser scans.
 73.70 % 84.56 % 68.09 % 0.04 s GPU @ 2.5 Ghz (Python)
64 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.
65 DDB
This method makes use of Velodyne laser scans.
 73.49 % 82.45 % 67.82 % 0.05 s GPU @ 2.5 Ghz (Python)
66 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++)
67 MP 73.32 % 84.00 % 67.82 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
68 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.
69 SAANet 73.14 % 84.30 % 66.28 % 0.10 s 1 core @ 2.5 Ghz (Python)
70 SFB-SECOND 73.07 % 83.66 % 68.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 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.
72 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++)
73 RUC 72.65 % 80.76 % 68.74 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
74 AILabs3D
This method makes use of Velodyne laser scans.
 72.63 % 83.85 % 63.75 % 0.6 s GPU @ >3.5 Ghz (Python)
75 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.
76 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++)
77 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.
78 MDC
This method makes use of Velodyne laser scans.
 71.47 % 82.66 % 62.27 % 0.17 s GPU @ 2.5 Ghz (Python)
79 PP_v1.0 code 70.34 % 80.15 % 64.58 % 0.02s 1 core @ 2.5 Ghz (C/C++)
80 PAD 70.33 % 78.94 % 64.83 % 0.15 s 1 core @ 2.5 Ghz (Python)
81 CONV-BOX
This method makes use of Velodyne laser scans.
 70.03 % 80.98 % 65.66 % 0.2 s Tesla V100
82 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.
83 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
 69.39 % 79.27 % 64.41 % 0.035 s GPU (C++)
84 RuiRUC 69.32 % 81.45 % 57.64 % 0.12 s 1 core @ 2.5 Ghz (Python)
85 DFD 69.20 % 79.84 % 62.32 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
86 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.
87 SCANet 68.12 % 78.65 % 61.44 % 0.17 s >8 cores @ 2.5 Ghz (Python)
88 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)
89 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++)
90 RTL3D 67.79 % 80.72 % 61.34 % 0.02 s GPU @ 2.5 Ghz (Python)
91 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.
92 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)
93 SCANet 67.13 % 79.22 % 60.65 % 0.09s GPU @ 2.5 Ghz (Python)
94 SECA 66.51 % 79.04 % 60.18 % 1 s GPU @ 2.5 Ghz (Python)
95 VSE 66.51 % 79.04 % 60.18 % 0.15 s GPU @ 2.5 Ghz (Python)
96 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.
97 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++)
98 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++)
99 FNV1_RPN 65.99 % 77.98 % 57.99 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
100 X_MD 65.89 % 77.52 % 59.79 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
101 SECA 65.53 % 76.87 % 59.18 % 0.09 s GPU @ 2.5 Ghz (Python)
102 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)
103 3DNN 64.74 % 76.32 % 58.10 % 0.09 s GPU @ 2.5 Ghz (Python)
104 NLK 64.49 % 76.78 % 59.37 % 0.02 s 1 core @ 2.5 Ghz (Python)
105 FNV1_Fusion 64.21 % 76.27 % 57.65 % 0.11 s GPU @ 2.5 Ghz (Python)
106 VoxelNet(Unofficial) 64.17 % 77.82 % 57.51 % 0.5 s GPU @ 2.0 Ghz (Python)
107 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.
108 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)
109 FNV2 60.35 % 69.39 % 50.96 % 0.18 s GPU @ 2.5 Ghz (Python)
110 FNV1 60.24 % 71.81 % 53.91 % 0.11 s GPU @ 2.5 Ghz (Python)
111 ANM 59.07 % 74.99 % 47.64 % 0.12 s 1 core @ 2.5 Ghz (Python)
112 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.
113 anm 56.17 % 70.34 % 48.11 % 3 s 1 core @ 2.5 Ghz (C/C++)
114 CLF3D
This method makes use of Velodyne laser scans.
 55.94 % 67.04 % 46.79 % 0.13 s GPU @ 2.5 Ghz (Python)
115 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++)
116 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.
117 avodC 54.03 % 67.80 % 47.95 % 0.1 s GPU @ 2.5 Ghz (Python)
118 E-VoxelNet 52.39 % 66.35 % 46.74 % 0.1 s GPU @ 2.5 Ghz (Python)
119 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.
120 Pseudo-LiDAR E2E
This method uses stereo information.
 43.92 % 64.75 % 38.14 % 0.4 s GPU @ 2.5 Ghz (Python)
121 Pseudo-LiDAR V2
This method uses stereo information.
 code 42.43 % 61.11 % 36.99 % 0.4 s GPU @ 2.5 Ghz (Python)
122 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++)
123 ZoomNet
This method uses stereo information.
 38.64 % 55.98 % 30.97 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
124 stereo_sa
This method uses stereo information.
 37.92 % 58.70 % 31.99 % 0.3 s GPU @ 2.5 Ghz (Python)
125 Disp R-CNN
This method uses stereo information.
 37.91 % 58.53 % 31.93 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
126 OC Stereo
This method uses stereo information.
 37.60 % 55.15 % 30.25 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
127 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.
128 m-prcnn
This method uses stereo information.
 31.21 % 53.96 % 24.52 % 0.43 s 1 core @ 2.5 Ghz (Python)
129 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.
130 30.06 % 48.89 % 24.70 %
131 SA_3D 29.61 % 40.77 % 23.86 % 0.3 s GPU @ 2.5 Ghz (Python)
132 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.
133 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.
134 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. 2019.
135 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)
136 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++)
137 Licar
This method makes use of Velodyne laser scans.
 12.10 % 15.23 % 11.39 % 0.09 s GPU @ 2.0 Ghz (Python)
138 D^4LCN 11.72 % 16.65 % 9.51 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
139 RefinedMPL 11.14 % 18.09 % 8.94 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
140 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.
141 MonoPair 9.99 % 13.04 % 8.65 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
142 SMOKE 9.76 % 14.03 % 7.84 % 0.03 s GPU @ 2.5 Ghz (Python)
143 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 .
144 MonoSS 9.61 % 13.74 % 7.75 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
145 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.
146 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.
147 RAR-Net 8.95 % 14.12 % 7.19 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
148 PG-MonoNet 8.52 % 13.24 % 6.73 % 0.19 s GPU @ 2.5 Ghz (Python)
149 DT3D 8.51 % 13.94 % 7.10 % 0,21s GPU @ 2.5 Ghz (Python)
150 MonoDIS 7.94 % 10.37 % 6.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
151 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.
152 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.
153 Decoupled-3D v2 7.28 % 11.68 % 5.69 % 0.08 s GPU @ 2.5 Ghz (C/C++)
154 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.
155 Decoupled-3D 7.02 % 11.08 % 5.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
156 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.
157 RADNet-Mono 5.80 % 8.42 % 4.62 % 0.1 s 1 core @ 2.5 Ghz (Python)
158 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.
159 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.
160 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. 2019.
161 mylsi-faster-rcnn 5.07 % 8.36 % 4.21 % 0.3 s 1 core @ 2.5 Ghz (Python)
162 OACV 4.77 % 8.13 % 3.78 % 0.23 s GPU @ 2.5 Ghz (Python)
163 mymask-rcnn 4.21 % 8.54 % 3.44 % 0.3 s 1 core @ 2.5 Ghz (Python)
164 FailNet-Mono 4.19 % 6.84 % 3.28 % 0.1 s 1 core @ 2.5 Ghz (Python)
165 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.
166 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.
167 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 0.18 s GPU @ 2.5 Ghz (Python)
168 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.
169 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.
170 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.
171 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.
172 MF3D 1.48 % 2.86 % 1.27 % 0.03 s GPU @ 2.5 Ghz (C/C++)
173 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.
174 OFT-Net 1.32 % 1.61 % 1.00 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
175 3DVSSD 0.73 % 0.87 % 0.63 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
176 monoref3d 0.04 % 0.08 % 0.04 % 0.1 s 1 core @ 2.5 Ghz (Python)
177 ref3D 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
178 ref3D 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
179 ANM 0.00 % 0.00 % 0.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
180 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 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.
2 TANet 44.34 % 53.72 % 40.49 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
3 Noah CV Lab - SSL 44.34 % 51.22 % 39.03 % 0.1 s GPU @ 2.5 Ghz (Python)
4 A-VoxelNet 44.30 % 53.66 % 40.43 % 0.029 s GPU @ 2.5 Ghz (Python)
5 Point-GNN
This method makes use of Velodyne laser scans.
 43.77 % 51.92 % 40.14 % 0.6 s GPU @ 2.5 Ghz (Python)
6 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.
7 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.
8 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.
9 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.
10 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.
11 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.
12 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.
13 CentrNet-v1
This method makes use of Velodyne laser scans.
 41.50 % 50.86 % 38.24 % 0.03 s GPU @ 2.5 Ghz (Python)
14 PiP 41.01 % 49.01 % 37.90 % 0.05 s 1 core @ 2.5 Ghz (Python)
15 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++)
16 DDB
This method makes use of Velodyne laser scans.
 40.40 % 49.03 % 37.04 % 0.05 s GPU @ 2.5 Ghz (Python)
17 PPFNet code 40.11 % 48.36 % 37.00 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
18 MDC
This method makes use of Velodyne laser scans.
 39.84 % 50.05 % 35.81 % 0.17 s GPU @ 2.5 Ghz (Python)
19 OHS 39.72 % 47.14 % 37.25 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
20 LDAM 39.55 % 45.15 % 37.27 % 0.05 s GPU @ 2.5 Ghz (C/C++)
21 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++)
22 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.
23 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.
24 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.
25 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.
26 CONV-BOX
This method makes use of Velodyne laser scans.
 38.12 % 45.69 % 34.55 % 0.2 s Tesla V100
27 SCANet 37.93 % 48.41 % 34.10 % 0.17 s >8 cores @ 2.5 Ghz (Python)
28 PP_v1.0 code 37.57 % 45.93 % 34.66 % 0.02s 1 core @ 2.5 Ghz (C/C++)
29 FOFNet
This method makes use of Velodyne laser scans.
 37.56 % 47.45 % 34.00 % 0.04 s GPU @ 2.5 Ghz (Python)
30 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.
31 VOXEL_FPN_HR 37.01 % 46.32 % 34.67 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
32 deprecated 36.25 % 47.69 % 32.18 % 0.05 s GPU @ 2.0 Ghz (Python)
33 HR-SECOND code 35.52 % 45.31 % 33.14 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
34 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++)
35 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.
36 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++)
37 MP 33.89 % 43.04 % 31.46 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
38 anm 32.98 % 43.55 % 29.12 % 3 s 1 core @ 2.5 Ghz (C/C++)
39 SAANet 30.61 % 38.50 % 27.35 % 0.10 s 1 core @ 2.5 Ghz (Python)
40 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.
41 X_MD 29.25 % 38.42 % 25.77 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
42 anonymous
This method makes use of Velodyne laser scans.
 28.35 % 36.94 % 24.99 % 0.75 s GPU @ 3.5 Ghz (C/C++)
43 SA_3D 28.04 % 36.11 % 24.81 % 0.3 s GPU @ 2.5 Ghz (Python)
44 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.
45 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.
46 27.75 % 35.85 % 25.09 %
47 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.
48 CLF3D
This method makes use of Velodyne laser scans.
 26.40 % 34.94 % 23.14 % 0.13 s GPU @ 2.5 Ghz (Python)
49 OC Stereo
This method uses stereo information.
 17.58 % 24.48 % 15.60 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
50 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.
51 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.
52 RefinedMPL 6.93 % 10.90 % 5.69 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
53 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.
54 MonoPair 6.68 % 10.02 % 5.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
55 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.
56 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.
57 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 .
58 D^4LCN 3.42 % 4.55 % 2.83 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
59 PG-MonoNet 2.58 % 3.61 % 2.36 % 0.19 s GPU @ 2.5 Ghz (Python)
60 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.
61 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.
62 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.
63 mylsi-faster-rcnn 0.96 % 1.36 % 0.62 % 0.3 s 1 core @ 2.5 Ghz (Python)
64 mymask-rcnn 0.80 % 1.16 % 0.71 % 0.3 s 1 core @ 2.5 Ghz (Python)
65 DT3D 0.37 % 0.57 % 0.35 % 0,21s GPU @ 2.5 Ghz (Python)
66 OFT-Net 0.36 % 0.63 % 0.35 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
67 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 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++)
4 Point-GNN
This method makes use of Velodyne laser scans.
 63.48 % 78.60 % 57.08 % 0.6 s GPU @ 2.5 Ghz (Python)
5 OHS 62.72 % 79.09 % 56.76 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
6 VOXEL_FPN_HR 61.91 % 78.29 % 55.54 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
7 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.
8 HR-SECOND code 60.82 % 75.83 % 53.67 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
9 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.
10 FOFNet
This method makes use of Velodyne laser scans.
 59.73 % 76.23 % 53.44 % 0.04 s GPU @ 2.5 Ghz (Python)
11 PiP 59.54 % 75.43 % 53.37 % 0.05 s 1 core @ 2.5 Ghz (Python)
12 TANet 59.44 % 75.70 % 52.53 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
13 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++)
14 MDC
This method makes use of Velodyne laser scans.
 59.02 % 75.54 % 50.56 % 0.17 s GPU @ 2.5 Ghz (Python)
15 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.
16 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.
17 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.
18 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++)
19 LDAM 56.79 % 71.66 % 50.82 % 0.05 s GPU @ 2.5 Ghz (C/C++)
20 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.
21 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.
22 A-VoxelNet 55.86 % 72.58 % 49.13 % 0.029 s GPU @ 2.5 Ghz (Python)
23 deprecated 55.58 % 77.86 % 48.49 % 0.05 s GPU @ 2.0 Ghz (Python)
24 MP 55.36 % 72.99 % 49.36 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
25 CONV-BOX
This method makes use of Velodyne laser scans.
 55.27 % 67.27 % 49.33 % 0.2 s Tesla V100
26 CentrNet-v1
This method makes use of Velodyne laser scans.
 54.64 % 72.03 % 48.03 % 0.03 s GPU @ 2.5 Ghz (Python)
27 SCANet 53.38 % 68.71 % 47.59 % 0.17 s >8 cores @ 2.5 Ghz (Python)
28 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.
29 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.
30 DDB
This method makes use of Velodyne laser scans.
 51.38 % 68.83 % 45.15 % 0.05 s GPU @ 2.5 Ghz (Python)
31 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.
32 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.
33 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++)
34 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.
35 PP_v1.0 code 48.86 % 66.46 % 42.59 % 0.02s 1 core @ 2.5 Ghz (C/C++)
36 SAANet 48.67 % 62.76 % 43.45 % 0.10 s 1 core @ 2.5 Ghz (Python)
37 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++)
38 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.
39 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.
40 X_MD 36.24 % 50.58 % 32.70 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
41 anm 33.28 % 49.27 % 28.90 % 3 s 1 core @ 2.5 Ghz (C/C++)
42 CLF3D
This method makes use of Velodyne laser scans.
 32.79 % 49.38 % 28.74 % 0.13 s GPU @ 2.5 Ghz (Python)
43 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.
44 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.
45 OC Stereo
This method uses stereo information.
 16.63 % 29.40 % 14.72 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
46 SA_3D 10.68 % 15.18 % 8.83 % 0.3 s GPU @ 2.5 Ghz (Python)
47 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.
48 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.
49 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.
50 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.
51 MonoPair 2.12 % 3.79 % 1.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
52 RefinedMPL 1.75 % 3.21 % 1.54 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
53 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.
54 D^4LCN 1.67 % 2.45 % 1.36 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
55 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.
56 mylsi-faster-rcnn 1.07 % 1.71 % 0.85 % 0.3 s 1 core @ 2.5 Ghz (Python)
57 PG-MonoNet 0.90 % 1.59 % 0.95 % 0.19 s GPU @ 2.5 Ghz (Python)
58 DT3D 0.69 % 0.97 % 0.73 % 0,21s GPU @ 2.5 Ghz (Python)
59 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 .
60 mymask-rcnn 0.30 % 0.71 % 0.28 % 0.3 s 1 core @ 2.5 Ghz (Python)
61 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.
62 OFT-Net 0.06 % 0.14 % 0.07 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
63 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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