Bird's Eye View Evaluation 2017


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

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

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 SA-SSD 91.03 % 95.03 % 85.96 % 0.04 s 1 core @ 2.5 Ghz (Python)
2 PV-RCNN
This method makes use of Velodyne laser scans.
90.65 % 94.98 % 86.14 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
3 STD 89.19 % 94.74 % 86.42 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
4 Point-GNN
This method makes use of Velodyne laser scans.
89.17 % 93.11 % 83.90 % 0.6 s GPU @ 2.5 Ghz (Python)
5 Noah CV Lab - SSL 89.10 % 92.01 % 81.72 % 0.1 s GPU @ 2.5 Ghz (Python)
6 ORP 89.07 % 93.03 % 81.79 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
7 3DSSD 89.02 % 92.66 % 85.86 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
8 MLF_PointCas 88.99 % 92.60 % 81.74 % 0.1 s GPU @ 2.5 Ghz (Python)
9 GPOD
This method makes use of Velodyne laser scans.
88.86 % 93.56 % 83.22 % 0.1 s GPU @ 2.5 Ghz (Python)
10 HVNet 88.82 % 92.83 % 83.38 % 0.03 s GPU @ 2.0 Ghz (Python)
11 ELE 88.80 % 94.52 % 85.69 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
12 RGB3D
This method makes use of Velodyne laser scans.
88.69 % 92.84 % 81.76 % 0.39 s GPU @ 2.5 Ghz (Python)
13 FCPP 88.65 % 92.36 % 83.21 % 0.02 s 1 core @ 2.0 Ghz (Python + C/C++)
14 DENFIDet 88.56 % 92.42 % 83.76 % 0.02 s GPU @ 2.5 Ghz (C/C++)
15 EPNet 88.47 % 94.22 % 83.69 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
16 Patches
This method makes use of Velodyne laser scans.
88.39 % 92.72 % 83.19 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
17 UberATG-MMF
This method makes use of Velodyne laser scans.
88.21 % 93.67 % 81.99 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
18 Patches - EMP
This method makes use of Velodyne laser scans.
88.17 % 94.49 % 84.75 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
19 PointPainting
This method makes use of Velodyne laser scans.
88.11 % 92.45 % 83.36 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. arXiv preprint arXiv:1911.10150 2019.
20 OHS 88.11 % 93.73 % 84.98 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
21 CPRCCNN 88.10 % 94.11 % 83.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
22 Associate-3Ddet
This method makes use of Velodyne laser scans.
88.09 % 91.40 % 82.96 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
23 DEFT 88.06 % 92.06 % 83.22 % 1 s GPU @ 2.5 Ghz (Python)
24 deprecated 88.05 % 91.96 % 83.21 % 0.05 s GPU @ >3.5 Ghz (Python)
25 3D-CVF
This method makes use of Velodyne laser scans.
88.04 % 91.97 % 83.22 % 0.05 s GPU @ >3.5 Ghz (Python)
26 UberATG-HDNET
This method makes use of Velodyne laser scans.
87.98 % 93.13 % 81.23 % 0.05 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
27 OHS + Occ 87.95 % 93.59 % 83.21 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
28 SPA 87.90 % 91.70 % 83.18 % 0.1 s 1 core @ 2.5 Ghz (Python)
29 Fast Point R-CNN v1
This method makes use of Velodyne laser scans.
87.84 % 90.87 % 80.52 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
30 MMLab-PartA^2
This method makes use of Velodyne laser scans.
87.79 % 91.70 % 84.61 % 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.
31 HRI-FusionRCNN 87.77 % 93.18 % 80.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
32 MODet
This method makes use of Velodyne laser scans.
87.56 % 90.80 % 82.69 % 0.05 s GTX1080Ti
33 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 87.53 % 91.99 % 81.03 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
34 PointRGCN 87.49 % 91.63 % 80.73 % 0.26 s GPU @ V100 (Python)
35 MLF_SecCas 87.46 % 92.54 % 77.39 % 0.05 s 1 core @ 2.5 Ghz (Python)
36 SAIC-SA-3D
This method makes use of Velodyne laser scans.
87.45 % 92.34 % 83.72 % 0.05 s GPU @ 2.5 Ghz (Python)
37 IE-PointRCNN 87.43 % 92.11 % 81.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 PC-CNN-V2
This method makes use of Velodyne laser scans.
87.40 % 91.19 % 79.35 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
39 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 87.39 % 92.13 % 82.72 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
40 MPNet
This method makes use of Velodyne laser scans.
87.31 % 91.27 % 83.25 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
41 deprecated 87.28 % 90.44 % 75.09 % 0.05 s GPU @ 2.0 Ghz (Python)
42 PiP 87.25 % 90.87 % 83.38 % 0.05 s 1 core @ 2.5 Ghz (Python)
43 HRI-VoxelFPN 87.21 % 92.75 % 79.82 % 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.
44 CentrNet-v1
This method makes use of Velodyne laser scans.
87.19 % 90.72 % 83.34 % 0.03 s GPU @ 2.5 Ghz (Python)
45 epBRM
This method makes use of Velodyne laser scans.
code 87.13 % 90.70 % 81.92 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
46 DH-ARI 87.13 % 90.26 % 80.52 % 0.2 s 1 core @ >3.5 Ghz (Python + C/C++)
47 Roadstar.ai 87.12 % 92.42 % 81.88 % 0.08 s GPU @ 2.0 Ghz (Python)
48 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
87.00 % 92.50 % 79.61 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
49 PCSC-Net 86.94 % 90.44 % 82.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
50 SARPNET 86.92 % 92.21 % 81.68 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
51 TBA 86.85 % 90.51 % 83.05 % 0.07 s 1 core @ 2.5 Ghz (Python)
52 ARPNET 86.81 % 90.06 % 79.41 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
53 PointPiallars_SECA 86.79 % 90.15 % 82.87 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
54 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
86.72 % 90.27 % 81.35 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
55 SRF 86.60 % 91.90 % 81.43 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
56 PointPillars
This method makes use of Velodyne laser scans.
code 86.56 % 90.07 % 82.81 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
57 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
86.55 % 91.88 % 79.23 % 0.2 s GPU @ >3.5 Ghz (Python)
58 TANet 86.54 % 91.58 % 81.19 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
59 A-VoxelNet 86.53 % 89.94 % 79.08 % 0.029 s GPU @ 2.5 Ghz (Python)
60 PointRCNN-deprecated
This method makes use of Velodyne laser scans.
86.52 % 92.51 % 81.39 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
61 SCNet
This method makes use of Velodyne laser scans.
86.48 % 90.07 % 81.30 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
62 MMV 86.46 % 90.04 % 79.04 % 0.4 s GPU @ 2.5 Ghz (C/C++)
63 RUC 86.46 % 90.06 % 82.20 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
64 DDB
This method makes use of Velodyne laser scans.
86.45 % 89.91 % 82.21 % 0.05 s GPU @ 2.5 Ghz (Python)
65 PPFNet code 86.44 % 92.35 % 81.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
66 HR-SECOND code 86.40 % 91.68 % 81.40 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
67 SECOND-V1.5
This method makes use of Velodyne laser scans.
code 86.37 % 91.81 % 81.04 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
68 SegVoxelNet 86.37 % 91.62 % 83.04 % 0.04 s 1 core @ 2.5 Ghz (Python)
69 VOXEL_FPN_HR 86.36 % 90.28 % 81.20 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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70 CP
This method makes use of Velodyne laser scans.
86.30 % 92.14 % 82.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 FOFNet
This method makes use of Velodyne laser scans.
86.22 % 90.09 % 78.96 % 0.04 s GPU @ 2.5 Ghz (Python)
72 NU-optim 86.22 % 91.62 % 80.81 % 0.04 s GPU @ >3.5 Ghz (Python)
73 3D IoU Loss
This method makes use of Velodyne laser scans.
86.22 % 91.36 % 81.20 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
74 MP 86.16 % 90.24 % 78.86 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
75 R-GCN 86.05 % 91.91 % 81.05 % 0.16 s GPU @ 2.5 Ghz (Python)
76 MVX-Net
This method makes use of Velodyne laser scans.
86.05 % 92.13 % 78.68 % 0.06 s GPU @ 3.0 Ghz (Python + C/C++)
77 UberATG-PIXOR++
This method makes use of Velodyne laser scans.
86.01 % 93.28 % 80.11 % 0.035 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
78 PTS
This method makes use of Velodyne laser scans.
code 85.95 % 91.42 % 80.81 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
79 F-ConvNet
This method makes use of Velodyne laser scans.
85.84 % 91.51 % 76.11 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
80 BVVF 85.83 % 91.20 % 80.76 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
81 PI-RCNN 85.81 % 91.44 % 81.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
82 SAANet 85.69 % 91.72 % 78.77 % 0.10 s 1 core @ 2.5 Ghz (Python)
83 SFB-SECOND 85.63 % 91.38 % 78.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 Fast Point R-CNN
This method makes use of Velodyne laser scans.
85.61 % 90.76 % 79.99 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
85 UberATG-ContFuse
This method makes use of Velodyne laser scans.
85.35 % 94.07 % 75.88 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
86 MDC
This method makes use of Velodyne laser scans.
85.29 % 91.63 % 75.54 % 0.17 s GPU @ 2.5 Ghz (Python)
87 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
85.10 % 88.65 % 78.22 % 0.035 s GPU (C++)
88 PFPN 85.02 % 90.68 % 77.47 % 0.02 s 4 cores @ >3.5 Ghz (Python)
89 AVOD
This method makes use of Velodyne laser scans.
code 84.95 % 89.75 % 78.32 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
90 CONV-BOX
This method makes use of Velodyne laser scans.
84.91 % 90.58 % 80.24 % 0.2 s Tesla V100
91 AVOD-FPN
This method makes use of Velodyne laser scans.
code 84.82 % 90.99 % 79.62 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
92 PP_v1.0 code 84.69 % 88.44 % 80.19 % 0.02s 1 core @ 2.5 Ghz (C/C++)
93 F-PointNet
This method makes use of Velodyne laser scans.
code 84.67 % 91.17 % 74.77 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
94 IPOD 84.62 % 89.64 % 79.96 % 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.
95 PAD 84.46 % 88.66 % 80.61 % 0.15 s 1 core @ 2.5 Ghz (Python)
96 CFR
This method makes use of Velodyne laser scans.
84.30 % 90.25 % 76.80 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
97 MVSLN 84.26 % 90.30 % 78.94 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
98 3DBN
This method makes use of Velodyne laser scans.
83.94 % 89.66 % 76.50 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
99 RADNet-Fusion
This method makes use of Velodyne laser scans.
83.84 % 91.81 % 78.80 % 0.1 s 1 core @ 2.5 Ghz (Python)
100 SECOND code 83.77 % 89.39 % 78.59 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
101 RADNet-LIDAR
This method makes use of Velodyne laser scans.
83.74 % 92.43 % 77.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
102 3DNN 83.68 % 88.06 % 77.00 % 0.09 s GPU @ 2.5 Ghz (Python)
103 AILabs3D
This method makes use of Velodyne laser scans.
83.57 % 91.46 % 76.05 % 0.6 s GPU @ >3.5 Ghz (Python)
104 DFD 83.20 % 88.56 % 77.84 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
105 SCANet 83.19 % 88.68 % 77.84 % 0.17 s >8 cores @ 2.5 Ghz (Python)
106 ELLIOT
This method makes use of Velodyne laser scans.
83.03 % 88.29 % 78.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
107 SCANet 82.85 % 90.33 % 76.06 % 0.09s GPU @ 2.5 Ghz (Python)
108 FailNet-Fusion
This method makes use of Velodyne laser scans.
82.78 % 93.20 % 75.84 % 0.1 s 1 core @ 2.5 Ghz (Python)
109 SECA 82.75 % 90.60 % 75.93 % 0.09 s GPU @ 2.5 Ghz (Python)
110 yl_net 82.70 % 87.27 % 80.23 % 0.03 s GPU @ 2.5 Ghz (Python)
111 MLOD
This method makes use of Velodyne laser scans.
code 82.68 % 90.25 % 77.97 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
112 SECA 82.58 % 90.37 % 75.75 % 1 s GPU @ 2.5 Ghz (Python)
113 VSE 82.58 % 90.37 % 75.75 % 0.15 s GPU @ 2.5 Ghz (Python)
114 FailNet-LIDAR
This method makes use of Velodyne laser scans.
82.41 % 92.85 % 75.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
115 tiny_rfdet code 81.93 % 86.57 % 75.83 % 0.01 s GPU @ 2.5 Ghz (Python)
116 NLK 81.93 % 89.93 % 76.80 % 0.02 s 1 core @ 2.5 Ghz (Python)
117 RTL3D 81.63 % 89.55 % 76.63 % 0.02 s GPU @ 2.5 Ghz (Python)
118 PointRes
This method makes use of Velodyne laser scans.
This method makes use of GPS/IMU information.
This is an online method (no batch processing).
81.60 % 90.60 % 76.03 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
119 FNV1_RPN 80.85 % 90.39 % 73.88 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
120 FNV1_Fusion 80.41 % 88.48 % 75.33 % 0.11 s GPU @ 2.5 Ghz (Python)
121 X_MD 80.32 % 90.26 % 73.54 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
122 RuiRUC 80.20 % 86.90 % 67.77 % 0.12 s 1 core @ 2.5 Ghz (Python)
123 UberATG-PIXOR
This method makes use of Velodyne laser scans.
80.01 % 83.97 % 74.31 % 0.035 s TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
124 FNV1 79.62 % 87.37 % 72.57 % 0.11 s GPU @ 2.5 Ghz (Python)
125 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
78.98 % 86.49 % 72.23 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
126 MV3D
This method makes use of Velodyne laser scans.
78.93 % 86.62 % 69.80 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
127 Multi-3D
This method makes use of Velodyne laser scans.
78.45 % 85.99 % 67.14 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
128 VoxelNet(Unofficial) 78.39 % 87.95 % 71.29 % 0.5 s GPU @ 2.0 Ghz (Python)
129 FNV2 76.69 % 82.57 % 65.60 % 0.18 s GPU @ 2.5 Ghz (Python)
130 ANM 75.40 % 84.78 % 61.28 % 0.12 s 1 core @ 2.5 Ghz (Python)
131 LaserNet 74.52 % 79.19 % 68.45 % 12 ms GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
132 PL V2 (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
73.80 % 84.61 % 65.59 % 0.6 s GPU @ 2.5 Ghz (C/C++)
133 anm 73.63 % 82.59 % 62.87 % 3 s 1 core @ 2.5 Ghz (C/C++)
134 A3DODWTDA
This method makes use of Velodyne laser scans.
code 73.26 % 79.58 % 62.77 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
135 CLF3D
This method makes use of Velodyne laser scans.
73.13 % 80.84 % 60.64 % 0.13 s GPU @ 2.5 Ghz (Python)
136 avodC 72.78 % 84.61 % 66.02 % 0.1 s GPU @ 2.5 Ghz (Python)
137 E-VoxelNet 69.69 % 81.10 % 60.88 % 0.1 s GPU @ 2.5 Ghz (Python)
138 FCY
This method makes use of Velodyne laser scans.
69.35 % 77.93 % 64.17 % 0.02 s GPU @ 2.5 Ghz (Python)
139 Complexer-YOLO
This method makes use of Velodyne laser scans.
68.96 % 77.24 % 64.95 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
140 TopNet-Retina
This method makes use of Velodyne laser scans.
68.16 % 80.16 % 63.43 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
141 DSGN
This method uses stereo information.
65.05 % 82.90 % 56.60 % 0.67 s NVIDIA Tesla V100
142 TopNet-DecayRate
This method makes use of Velodyne laser scans.
64.60 % 79.74 % 58.04 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
143 3D FCN
This method makes use of Velodyne laser scans.
61.67 % 70.62 % 55.61 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
144 TopNet-UncEst
This method makes use of Velodyne laser scans.
59.67 % 72.05 % 51.67 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
145 Pseudo-LiDAR E2E
This method uses stereo information.
58.84 % 79.58 % 52.06 % 0.4 s GPU @ 2.5 Ghz (Python)
146 Pseudo-LiDAR V2
This method uses stereo information.
code 58.01 % 78.31 % 51.25 % 0.4 s GPU @ 2.5 Ghz (Python)
147 ZoomNet
This method uses stereo information.
code 54.91 % 72.94 % 44.14 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
148 VoxelJones code 53.96 % 66.21 % 47.66 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
149 TopNet-HighRes
This method makes use of Velodyne laser scans.
53.05 % 67.84 % 46.99 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
150 Disp R-CNN (velo)
This method uses stereo information.
52.34 % 74.07 % 43.77 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
151 Disp R-CNN
This method uses stereo information.
52.34 % 73.82 % 43.64 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
152 BirdNet
This method makes use of Velodyne laser scans.
51.51 % 76.88 % 50.27 % 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.
153 OC Stereo
This method uses stereo information.
51.47 % 68.89 % 42.97 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
154 stereo_sa
This method uses stereo information.
49.61 % 71.47 % 42.71 % 0.3 s GPU @ 2.5 Ghz (Python)
155 SA_3D 47.52 % 59.69 % 38.70 % 0.3 s GPU @ 2.5 Ghz (Python)
156 RT3DStereo
This method uses stereo information.
46.82 % 58.81 % 38.38 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
157 Pseudo-LiDAR
This method uses stereo information.
code 45.00 % 67.30 % 38.40 % 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.
158 RT3D
This method makes use of Velodyne laser scans.
44.00 % 56.44 % 42.34 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
159 m-prcnn
This method uses stereo information.
42.81 % 67.82 % 33.63 % 0.43 s 1 core @ 2.5 Ghz (Python)
160 42.22 % 64.44 % 35.61 %
161 Stereo R-CNN
This method uses stereo information.
code 41.31 % 61.92 % 33.42 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
162 Licar
This method makes use of Velodyne laser scans.
38.47 % 46.67 % 35.78 % 0.09 s GPU @ 2.0 Ghz (Python)
163 DLMB
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
34.53 % 45.90 % 31.83 % 0.03 s 8 cores @ 3.5 Ghz (C/C++)
164 ASOD 33.63 % 54.61 % 26.76 % 0.28 s GPU @ 2.5 Ghz (Python)
165 StereoFENet
This method uses stereo information.
32.96 % 49.29 % 25.90 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
166 RefinedMPL 17.60 % 28.08 % 13.95 % 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.
167 AM3D 17.32 % 25.03 % 14.91 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
168 D4LCN code 16.02 % 22.51 % 12.55 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. arXiv preprint arXiv:1912.04799 2019.
169 MonoPair 14.83 % 19.28 % 12.89 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
170 Decoupled-3D 14.82 % 23.16 % 11.25 % 0.08 s GPU @ 2.5 Ghz (C/C++)
171 Decoupled-3D v2 14.66 % 24.62 % 11.46 % 0.08 s GPU @ 2.5 Ghz (C/C++)
172 DT3D 14.57 % 22.52 % 12.76 % 0,21s GPU @ 2.5 Ghz (Python)
173 MonoSS 14.52 % 20.91 % 12.63 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
174 SMOKE 14.49 % 20.83 % 12.75 % 0.03 s GPU @ 2.5 Ghz (Python)
175 Mono3D_PLiDAR code 13.92 % 21.27 % 11.25 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
176 SS3D_HW 13.70 % 20.28 % 9.86 % 0.4 s GPU @ 2.5 Ghz (Python)
177 M3D-RPN code 13.67 % 21.02 % 10.23 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
178 RAR-Net 13.55 % 20.70 % 10.13 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
179 MonoDIS 13.19 % 17.23 % 11.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
180 CSoR
This method makes use of Velodyne laser scans.
13.07 % 18.67 % 10.34 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
181 MonoPSR code 12.58 % 18.33 % 9.91 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
182 PG-MonoNet 12.45 % 19.79 % 9.68 % 0.19 s GPU @ 2.5 Ghz (Python)
183 SS3D 11.52 % 16.33 % 9.93 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
184 MonoGRNet code 11.17 % 18.19 % 8.73 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
185 MonoFENet 11.03 % 17.03 % 9.05 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
186 RADNet-Mono 10.57 % 15.22 % 8.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
187 OACV 10.13 % 16.24 % 8.28 % 0.23 s GPU @ 2.5 Ghz (Python)
188 mylsi-faster-rcnn 9.96 % 15.55 % 8.11 % 0.3 s 1 core @ 2.5 Ghz (Python)
189 FailNet-Mono 9.11 % 14.41 % 7.11 % 0.1 s 1 core @ 2.5 Ghz (Python)
190 A3DODWTDA (image) code 8.66 % 10.37 % 7.06 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
191 mymask-rcnn 8.29 % 15.56 % 6.53 % 0.3 s 1 core @ 2.5 Ghz (Python)
192 Shift R-CNN (mono) code 6.82 % 11.84 % 5.27 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
193 GS3D 6.08 % 8.41 % 4.94 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
194 MVRA + I-FRCNN+ 5.84 % 9.05 % 4.50 % 0.18 s GPU @ 2.5 Ghz (Python)
195 OFT-Net 5.69 % 7.16 % 4.61 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
196 ROI-10D 4.91 % 9.78 % 3.74 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
197 MF3D 3.49 % 6.39 % 2.69 % 0.03 s GPU @ 2.5 Ghz (C/C++)
198 FQNet 3.23 % 5.40 % 2.46 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
199 3D-SSMFCNN code 2.63 % 3.20 % 2.40 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
200 monoref3d 1.70 % 3.22 % 1.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
201 ref3D 1.70 % 3.22 % 1.21 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
202 3DVSSD 1.31 % 1.74 % 1.08 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
203 VeloFCN
This method makes use of Velodyne laser scans.
0.14 % 0.02 % 0.21 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
204 ANM 0.00 % 0.00 % 0.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
205 ref3D 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
206 multi-task CNN 0.00 % 0.00 % 0.00 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
207 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 DENFIDet 51.96 % 61.15 % 49.03 % 0.02 s GPU @ 2.5 Ghz (C/C++)
2 A-VoxelNet 51.79 % 61.34 % 47.93 % 0.029 s GPU @ 2.5 Ghz (Python)
3 TANet 51.38 % 60.85 % 47.54 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
4 VMVS
This method makes use of Velodyne laser scans.
50.34 % 60.34 % 46.45 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
5 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.32 % 58.49 % 46.98 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
6 PointPainting
This method makes use of Velodyne laser scans.
49.93 % 58.70 % 46.29 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. arXiv preprint arXiv:1911.10150 2019.
7 MMLab-PartA^2
This method makes use of Velodyne laser scans.
49.81 % 59.04 % 45.92 % 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.
8 IPOD 49.79 % 60.88 % 45.43 % 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.
9 F-PointNet
This method makes use of Velodyne laser scans.
code 49.57 % 57.13 % 45.48 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
10 OHS + Occ 49.48 % 55.90 % 45.79 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
11 F-ConvNet
This method makes use of Velodyne laser scans.
48.96 % 57.04 % 44.33 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
12 HVNet 48.86 % 54.84 % 46.33 % 0.03 s GPU @ 2.0 Ghz (Python)
13 CentrNet-v1
This method makes use of Velodyne laser scans.
48.78 % 57.58 % 45.94 % 0.03 s GPU @ 2.5 Ghz (Python)
14 STD 48.72 % 60.02 % 44.55 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
15 PointPillars
This method makes use of Velodyne laser scans.
code 48.64 % 57.60 % 45.78 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
16 DDB
This method makes use of Velodyne laser scans.
48.35 % 57.68 % 45.44 % 0.05 s GPU @ 2.5 Ghz (Python)
17 PiP 48.14 % 56.16 % 45.27 % 0.05 s 1 core @ 2.5 Ghz (Python)
18 Noah CV Lab - SSL 47.94 % 54.74 % 43.78 % 0.1 s GPU @ 2.5 Ghz (Python)
19 PPFNet code 47.92 % 55.04 % 44.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
20 LDAM 47.35 % 52.08 % 45.23 % 0.05 s GPU @ 2.5 Ghz (C/C++)
21 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
47.24 % 56.06 % 44.61 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
22 Point-GNN
This method makes use of Velodyne laser scans.
47.07 % 55.36 % 44.61 % 0.6 s GPU @ 2.5 Ghz (Python)
23 SCNet
This method makes use of Velodyne laser scans.
46.73 % 56.87 % 42.74 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
24 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 46.13 % 54.77 % 42.84 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
25 Multi-3D
This method makes use of Velodyne laser scans.
46.09 % 54.37 % 41.42 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
26 ARPNET 45.92 % 55.48 % 42.54 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
27 PP_v1.0 code 45.73 % 53.93 % 43.05 % 0.02s 1 core @ 2.5 Ghz (C/C++)
28 epBRM
This method makes use of Velodyne laser scans.
code 45.49 % 52.48 % 42.75 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
29 MLOD
This method makes use of Velodyne laser scans.
code 45.40 % 55.09 % 41.42 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
30 MDC
This method makes use of Velodyne laser scans.
45.23 % 54.48 % 41.11 % 0.17 s GPU @ 2.5 Ghz (Python)
31 SECOND code 45.02 % 55.99 % 40.93 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
32 CONV-BOX
This method makes use of Velodyne laser scans.
44.84 % 52.98 % 42.30 % 0.2 s Tesla V100
33 OHS 44.59 % 50.87 % 42.14 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
34 GPOD
This method makes use of Velodyne laser scans.
44.55 % 52.13 % 42.12 % 0.1 s GPU @ 2.5 Ghz (Python)
35 Roadstar.ai 44.35 % 49.63 % 41.39 % 0.08 s GPU @ 2.0 Ghz (Python)
36 SCANet 42.81 % 53.84 % 38.94 % 0.17 s >8 cores @ 2.5 Ghz (Python)
37 FOFNet
This method makes use of Velodyne laser scans.
42.31 % 51.39 % 38.81 % 0.04 s GPU @ 2.5 Ghz (Python)
38 VOXEL_FPN_HR 41.62 % 50.18 % 38.30 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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39 deprecated 41.32 % 53.09 % 37.16 % 0.05 s GPU @ 2.0 Ghz (Python)
40 ELLIOT
This method makes use of Velodyne laser scans.
40.22 % 49.57 % 37.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
41 HR-SECOND code 40.06 % 50.05 % 36.47 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
42 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 38.79 % 47.51 % 35.85 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
43 MP 38.77 % 47.59 % 35.50 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
44 CFR
This method makes use of Velodyne laser scans.
38.74 % 50.64 % 36.23 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
45 anm 38.01 % 49.07 % 34.00 % 3 s 1 core @ 2.5 Ghz (C/C++)
46 CSW3D
This method makes use of Velodyne laser scans.
37.96 % 49.27 % 33.83 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
47 SA_3D 34.34 % 44.65 % 30.78 % 0.3 s GPU @ 2.5 Ghz (Python)
48 SparsePool code 34.15 % 43.33 % 31.78 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
49 SAANet 33.94 % 42.34 % 31.75 % 0.10 s 1 core @ 2.5 Ghz (Python)
50 AVOD
This method makes use of Velodyne laser scans.
code 33.57 % 42.58 % 30.14 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
51 anonymous
This method makes use of Velodyne laser scans.
33.47 % 42.38 % 29.97 % 0.75 s GPU @ 3.5 Ghz (C/C++)
52 SparsePool code 33.22 % 41.55 % 29.66 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
53 X_MD 32.57 % 42.18 % 30.23 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
54 32.32 % 40.87 % 29.52 %
55 CLF3D
This method makes use of Velodyne laser scans.
31.31 % 40.72 % 27.80 % 0.13 s GPU @ 2.5 Ghz (Python)
56 OC Stereo
This method uses stereo information.
20.80 % 29.79 % 18.62 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
57 DSGN
This method uses stereo information.
20.75 % 26.61 % 18.86 % 0.67 s NVIDIA Tesla V100
58 FCY
This method makes use of Velodyne laser scans.
19.04 % 23.98 % 17.83 % 0.02 s GPU @ 2.5 Ghz (Python)
59 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.26 % 21.42 % 17.06 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
60 BirdNet
This method makes use of Velodyne laser scans.
15.80 % 20.73 % 14.59 % 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.
61 TopNet-Retina
This method makes use of Velodyne laser scans.
14.57 % 18.04 % 12.48 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
62 TopNet-HighRes
This method makes use of Velodyne laser scans.
13.50 % 19.43 % 11.93 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
63 RefinedMPL 7.92 % 13.09 % 7.25 % 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.
64 MonoPair 7.04 % 10.99 % 6.29 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
65 TopNet-DecayRate
This method makes use of Velodyne laser scans.
6.59 % 8.78 % 6.25 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
66 Shift R-CNN (mono) code 5.66 % 8.58 % 4.49 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
67 SS3D_HW 5.47 % 8.81 % 4.79 % 0.4 s GPU @ 2.5 Ghz (Python)
68 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.60 % 6.88 % 3.79 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
69 MonoPSR code 4.56 % 7.24 % 4.11 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
70 M3D-RPN code 4.05 % 5.65 % 3.29 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
71 D4LCN code 3.86 % 5.06 % 3.59 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. arXiv preprint arXiv:1912.04799 2019.
72 RT3DStereo
This method uses stereo information.
3.65 % 4.72 % 3.00 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
73 PG-MonoNet 3.16 % 4.28 % 2.57 % 0.19 s GPU @ 2.5 Ghz (Python)
74 mylsi-faster-rcnn 2.11 % 3.08 % 1.89 % 0.3 s 1 core @ 2.5 Ghz (Python)
75 SS3D 2.09 % 2.48 % 1.61 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
76 mymask-rcnn 1.60 % 2.60 % 1.48 % 0.3 s 1 core @ 2.5 Ghz (Python)
77 OFT-Net 0.81 % 1.28 % 0.51 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
78 DT3D 0.51 % 0.75 % 0.48 % 0,21s GPU @ 2.5 Ghz (Python)
79 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 Noah CV Lab - SSL 74.27 % 85.74 % 64.03 % 0.1 s GPU @ 2.5 Ghz (Python)
2 PointPainting
This method makes use of Velodyne laser scans.
71.54 % 83.91 % 62.97 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. arXiv preprint arXiv:1911.10150 2019.
3 HVNet 71.17 % 83.97 % 63.65 % 0.03 s GPU @ 2.0 Ghz (Python)
4 PV-RCNN
This method makes use of Velodyne laser scans.
68.89 % 82.49 % 62.41 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
5 F-ConvNet
This method makes use of Velodyne laser scans.
68.88 % 84.16 % 60.05 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
6 MMLab-PartA^2
This method makes use of Velodyne laser scans.
68.73 % 83.43 % 61.85 % 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.
7 Point-GNN
This method makes use of Velodyne laser scans.
67.28 % 81.17 % 59.67 % 0.6 s GPU @ 2.5 Ghz (Python)
8 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 67.24 % 82.56 % 60.28 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
9 STD 67.23 % 81.36 % 59.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
10 OHS + Occ 67.20 % 79.66 % 61.04 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
11 OHS 66.86 % 82.13 % 60.86 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
12 ARPNET 66.39 % 82.32 % 58.80 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
13 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 65.85 % 80.00 % 58.69 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
14 DENFIDet 65.49 % 82.13 % 57.76 % 0.02 s GPU @ 2.5 Ghz (C/C++)
15 PiP 65.12 % 79.51 % 58.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
16 FOFNet
This method makes use of Velodyne laser scans.
65.06 % 80.44 % 57.55 % 0.04 s GPU @ 2.5 Ghz (Python)
17 VOXEL_FPN_HR 65.02 % 81.07 % 58.44 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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18 HR-SECOND code 64.21 % 78.79 % 57.82 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
19 Multi-3D
This method makes use of Velodyne laser scans.
64.09 % 80.81 % 54.67 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
20 TANet 63.77 % 79.16 % 56.21 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
21 LDAM 63.17 % 77.22 % 57.34 % 0.05 s GPU @ 2.5 Ghz (C/C++)
22 PointPillars
This method makes use of Velodyne laser scans.
code 62.73 % 79.90 % 55.58 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
23 MDC
This method makes use of Velodyne laser scans.
62.68 % 79.44 % 53.86 % 0.17 s GPU @ 2.5 Ghz (Python)
24 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
62.34 % 78.91 % 55.37 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
25 F-PointNet
This method makes use of Velodyne laser scans.
code 61.37 % 77.26 % 53.78 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
26 CONV-BOX
This method makes use of Velodyne laser scans.
61.01 % 71.70 % 54.69 % 0.2 s Tesla V100
27 A-VoxelNet 60.71 % 76.90 % 53.62 % 0.029 s GPU @ 2.5 Ghz (Python)
28 MP 60.16 % 77.57 % 54.01 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
29 GPOD
This method makes use of Velodyne laser scans.
60.03 % 69.36 % 54.39 % 0.1 s GPU @ 2.5 Ghz (Python)
30 epBRM
This method makes use of Velodyne laser scans.
code 59.79 % 75.13 % 53.36 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
31 Roadstar.ai 59.50 % 68.73 % 53.60 % 0.08 s GPU @ 2.0 Ghz (Python)
32 IPOD 59.40 % 78.19 % 51.38 % 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.
33 CentrNet-v1
This method makes use of Velodyne laser scans.
58.05 % 75.80 % 51.17 % 0.03 s GPU @ 2.5 Ghz (Python)
34 SAANet 57.98 % 74.71 % 51.95 % 0.10 s 1 core @ 2.5 Ghz (Python)
35 ELLIOT
This method makes use of Velodyne laser scans.
57.35 % 76.94 % 51.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
36 SCANet 57.20 % 72.86 % 51.16 % 0.17 s >8 cores @ 2.5 Ghz (Python)
37 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.12 % 69.39 % 51.09 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
38 DDB
This method makes use of Velodyne laser scans.
57.01 % 73.70 % 50.71 % 0.05 s GPU @ 2.5 Ghz (Python)
39 deprecated 56.42 % 81.02 % 49.28 % 0.05 s GPU @ 2.0 Ghz (Python)
40 SCNet
This method makes use of Velodyne laser scans.
56.39 % 73.73 % 49.99 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
41 SECOND code 56.05 % 76.50 % 49.45 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
42 MLOD
This method makes use of Velodyne laser scans.
code 55.06 % 73.03 % 48.21 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
43 PP_v1.0 code 53.85 % 72.02 % 47.21 % 0.02s 1 core @ 2.5 Ghz (C/C++)
44 CFR
This method makes use of Velodyne laser scans.
52.78 % 68.47 % 45.55 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
45 AVOD
This method makes use of Velodyne laser scans.
code 48.15 % 64.11 % 42.37 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
46 X_MD 41.47 % 55.29 % 36.10 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
47 SparsePool code 40.74 % 56.52 % 36.68 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
48 anm 38.56 % 56.94 % 34.06 % 3 s 1 core @ 2.5 Ghz (C/C++)
49 CLF3D
This method makes use of Velodyne laser scans.
37.40 % 53.98 % 32.06 % 0.13 s GPU @ 2.5 Ghz (Python)
50 TopNet-Retina
This method makes use of Velodyne laser scans.
36.83 % 47.48 % 33.58 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
51 SparsePool code 35.24 % 43.55 % 30.15 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
52 FCY
This method makes use of Velodyne laser scans.
30.10 % 44.87 % 27.17 % 0.02 s GPU @ 2.5 Ghz (Python)
53 Complexer-YOLO
This method makes use of Velodyne laser scans.
25.43 % 32.00 % 22.88 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
54 BirdNet
This method makes use of Velodyne laser scans.
23.78 % 36.01 % 21.09 % 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.
55 DSGN
This method uses stereo information.
21.04 % 31.23 % 18.93 % 0.67 s NVIDIA Tesla V100
56 OC Stereo
This method uses stereo information.
19.23 % 32.47 % 17.11 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
57 SA_3D 16.91 % 24.56 % 14.06 % 0.3 s GPU @ 2.5 Ghz (Python)
58 TopNet-DecayRate
This method makes use of Velodyne laser scans.
16.00 % 23.02 % 13.24 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
59 TopNet-UncEst
This method makes use of Velodyne laser scans.
9.18 % 12.31 % 8.14 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
60 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.48 % 9.99 % 6.76 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
61 MonoPSR code 5.78 % 9.87 % 4.57 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
62 RT3DStereo
This method uses stereo information.
4.10 % 7.03 % 3.88 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
63 MonoPair 2.87 % 4.76 % 2.42 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
64 SS3D_HW 2.78 % 5.03 % 2.36 % 0.4 s GPU @ 2.5 Ghz (Python)
65 RefinedMPL 2.42 % 4.23 % 2.14 % 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.
66 SS3D 1.89 % 3.45 % 1.44 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
67 D4LCN code 1.82 % 2.72 % 1.79 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. arXiv preprint arXiv:1912.04799 2019.
68 mylsi-faster-rcnn 1.54 % 2.41 % 1.41 % 0.3 s 1 core @ 2.5 Ghz (Python)
69 PG-MonoNet 1.24 % 1.96 % 1.01 % 0.19 s GPU @ 2.5 Ghz (Python)
70 M3D-RPN code 0.81 % 1.25 % 0.78 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
71 DT3D 0.77 % 1.49 % 0.85 % 0,21s GPU @ 2.5 Ghz (Python)
72 mymask-rcnn 0.71 % 1.39 % 0.69 % 0.3 s 1 core @ 2.5 Ghz (Python)
73 Shift R-CNN (mono) code 0.38 % 0.76 % 0.41 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
74 OFT-Net 0.16 % 0.36 % 0.15 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
75 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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