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 HRI-ADLab-HZ 82.83 % 89.00 % 76.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 SE-SSD
This method makes use of Velodyne laser scans.
82.54 % 91.49 % 77.15 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
3 EA-M-RCNN(BorderAtt) 82.33 % 87.77 % 77.37 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
4 HUAWEI Octopus 82.13 % 88.26 % 77.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 ADLAB 82.08 % 90.92 % 77.36 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
6 PV-RCNN-v2 81.88 % 90.14 % 77.15 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
7 RangeRCNN-LV 81.85 % 88.76 % 77.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 PVGNet 81.81 % 89.94 % 77.09 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
9 Fast VP-RCNN code 81.63 % 90.26 % 76.88 % 0.05 s 1 core @ >3.5 Ghz (python)
10 Voxel R-CNN 81.62 % 90.90 % 77.06 % 0.04 s GPU @ 3.0 Ghz (C/C++)
11 CVRS VIC-RCNN 81.57 % 88.60 % 77.09 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
12 SimpleDET 81.50 % 87.83 % 77.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
13 MSG-PGNN 81.48 % 87.78 % 77.02 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
14 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
81.46 % 88.25 % 76.96 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
15 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 81.43 % 90.25 % 76.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
16 PC-RGNN 81.38 % 87.94 % 76.88 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
17 RangeRCNN
This method makes use of Velodyne laser scans.
81.33 % 88.47 % 77.09 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
18 FSA-PVRCNN
This method makes use of Velodyne laser scans.
81.31 % 88.01 % 76.75 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
19 ReFineNet 81.24 % 87.70 % 76.77 % 0.08 s 1 core @ 2.5 Ghz (Python)
20 HyBrid Feature Det 81.16 % 87.99 % 76.81 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
21 MSL3D 81.15 % 87.27 % 76.56 % 0.03 s GPU @ 2.5 Ghz (Python)
22 Multi-Sensor3D 81.15 % 87.27 % 76.56 % 0.03 s GPU @ 2.5 Ghz (Python)
23 SVGA-Net
This method makes use of Velodyne laser scans.
80.82 % 87.40 % 76.23 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
24 Associate-3Ddet_v2 80.77 % 91.53 % 75.23 % 0.04 s 1 core @ 2.5 Ghz (Python)
25 CIA-SSD v2
This method makes use of Velodyne laser scans.
80.71 % 89.61 % 75.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
26 CLOCs_PVCas 80.67 % 88.94 % 77.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
27 AIMC-RUC 80.63 % 89.90 % 75.32 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
28 OAP 80.63 % 89.18 % 73.04 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
29 HRI-MSP-L
This method makes use of Velodyne laser scans.
80.62 % 87.61 % 76.29 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
30 CVRS VIC-Net 80.61 % 88.25 % 75.83 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
31 CVIS-DF3D_v2 80.48 % 87.20 % 76.01 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
32 XView-PartA^2 80.41 % 87.72 % 76.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
33 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
80.38 % 87.73 % 76.27 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
34 SPANet 80.34 % 91.05 % 74.89 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
35 CVRS_PF 80.33 % 88.04 % 75.21 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
36 CIA-SSD
This method makes use of Velodyne laser scans.
80.28 % 89.59 % 72.87 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
37 Baseline of CA RCNN 80.28 % 87.45 % 76.21 % 0.1 s GPU @ 2.5 Ghz (Python)
38 CVIS-DF3D 80.28 % 87.45 % 76.21 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
39 GAP-soft-filter 80.18 % 87.43 % 76.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 CBi-GNN 80.18 % 91.50 % 74.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
41 deprecated 80.16 % 89.48 % 72.75 % deprecated deprecated
42 EBM3DOD 80.12 % 91.05 % 72.78 % 0.08 s 1 core @ 2.5 Ghz (Python)
43 3D-CVF at SPA
This method makes use of Velodyne laser scans.
80.05 % 89.20 % 73.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
44 CN 79.89 % 90.55 % 76.31 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
45 VAL 79.87 % 89.35 % 70.27 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
46 RangeIoUDet
This method makes use of Velodyne laser scans.
79.80 % 88.60 % 76.76 % 0.02 s 1 core @ 2.5 Ghz (Python)
47 SA-SSD code 79.79 % 88.75 % 74.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
48 LZnet 79.73 % 89.16 % 72.28 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
49 CJJ 79.72 % 88.98 % 74.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
50 STD code 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.
51 ISF 79.71 % 89.13 % 74.78 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
52 AF_V1 79.68 % 88.16 % 72.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
53 FCY
This method makes use of Velodyne laser scans.
79.67 % 89.19 % 74.35 % 0.02 s GPU @ 2.5 Ghz (Python)
54 scssd-normal(0.3) 79.59 % 88.97 % 72.51 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.
55 3DSSD code 79.57 % 88.36 % 74.55 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
56 PointRes
This method makes use of Velodyne laser scans.
79.55 % 88.73 % 74.17 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
57 EBM3DOD baseline 79.52 % 88.80 % 72.30 % 0.08 s 1 core @ 2.5 Ghz (Python)
58 Cas-SSD 79.50 % 88.73 % 72.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 scssd-normal(0.4) 79.49 % 88.70 % 74.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.
60 Point-GNN
This method makes use of Velodyne laser scans.
code 79.47 % 88.33 % 72.29 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
61 PP-3D 79.47 % 88.33 % 72.29 % 0.1 s 1 core @ 2.5 Ghz (Python)
62 nonet 79.42 % 88.28 % 75.77 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
63 RoIFusion code 79.41 % 88.43 % 72.58 % 0.22 s 1 core @ 3.0 Ghz (Python)
64 EPNet code 79.28 % 89.81 % 74.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
65 PF-GAP 79.27 % 87.65 % 76.43 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
66 MGACNet 79.18 % 86.20 % 74.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
67 D3D 79.15 % 87.07 % 73.79 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
68 NLK-ALL code 79.13 % 87.23 % 74.30 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
69 3D IoU-Net 79.03 % 87.96 % 72.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
70 Noah CV Lab - SSL 78.99 % 85.50 % 71.75 % 0.1 s GPU @ 2.5 Ghz (Python)
71 CCFNET 78.97 % 88.20 % 74.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 SERCNN
This method makes use of Velodyne laser scans.
78.96 % 87.74 % 74.30 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
73 deprecated 78.83 % 87.89 % 73.52 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
74 FLID 78.78 % 86.73 % 71.24 % 0.04 s GPU @ 2.5 Ghz (Python)
75 ISF-v2 78.67 % 87.54 % 74.03 % 0.04 s 1 core @ 2.5 Ghz (Python)
76 PVF-NET 78.58 % 87.05 % 71.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
77 BLPNet_V2 78.57 % 87.10 % 71.67 % 0.04 s 1 core @ 2.5 Ghz (Python)
78 Discrete-PointDet 78.51 % 88.53 % 71.29 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
79 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.49 % 87.81 % 73.51 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
80 F-3DNet 78.48 % 85.48 % 71.62 % 0.5 s GPU @ 2.5 Ghz (Python)
81 CLOCs_SecCas 78.45 % 86.38 % 72.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
82 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.
83 LZY_RCNN 78.41 % 85.38 % 74.04 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
84 deprecated 78.32 % 89.34 % 71.21 % 0.06 s GPU @ >3.5 Ghz (Python)
85 HotSpotNet 78.31 % 87.60 % 73.34 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
86 cvMax 78.28 % 86.60 % 71.60 % 0.04 s GPU @ >3.5 Ghz (Python)
87 KNN-GCNN 78.26 % 86.37 % 71.14 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
88 Chovy 78.02 % 86.86 % 73.20 % 0.04 s GPU @ 2.5 Ghz (Python)
89 deprecated 77.97 % 86.76 % 73.00 % 0.04 s GPU @ 2.5 Ghz (Python)
90 HVPR 77.92 % 86.38 % 73.04 % 0.02 s GPU @ 2.5 Ghz (Python)
91 CenterNet3D 77.90 % 86.20 % 73.03 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
92 V3D 77.87 % 86.58 % 72.52 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
93 tbd code 77.72 % 86.09 % 72.53 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
94 VOXEL_3D 77.69 % 86.45 % 72.20 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
95 PPBA 77.65 % 84.16 % 71.21 % NA s GPU @ 2.5 Ghz (Python)
96 TBU 77.65 % 84.16 % 71.21 % NA s GPU @ 2.5 Ghz (Python)
97 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.
98 Associate-3Ddet code 77.40 % 85.99 % 70.53 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
99 Fast Point R-CNN
This method makes use of Velodyne laser scans.
77.40 % 85.29 % 70.24 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
100 Dccnet 77.22 % 86.67 % 69.97 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
101 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.
102 MVAF-Net code 77.08 % 85.64 % 70.39 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. 2020.
103 VAR 77.08 % 84.92 % 72.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
104 CU-PointRCNN 76.87 % 86.55 % 73.17 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
105 HRI-VoxelFPN 76.70 % 85.64 % 69.44 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
106 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.
107 TBD 76.57 % 85.33 % 72.05 % 0.05 s GPU @ 2.5 Ghz (Python)
108 IGRP+ 76.54 % 86.90 % 71.77 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
109 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.
110 F-ConvNet
This method makes use of Velodyne laser scans.
code 76.39 % 87.36 % 66.69 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
111 VICNet 76.18 % 85.21 % 70.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
112 SegVoxelNet 76.13 % 86.04 % 70.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
113 NLK-3D 76.08 % 84.47 % 70.93 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
114 TANet code 75.94 % 84.39 % 68.82 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
115 IGRP 75.90 % 86.27 % 69.31 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
116 MVX-Net++ 75.86 % 85.99 % 70.70 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
117 PointCSE 75.82 % 86.46 % 70.47 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
118 PointRGCN 75.73 % 85.97 % 70.60 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
119 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.
120 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.
121 MDA 75.39 % 83.72 % 71.98 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
122 R-GCN 75.26 % 83.42 % 68.73 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
123 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.
124 MuRF 75.11 % 84.81 % 69.99 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
125 3DBN_2 75.06 % 84.90 % 72.10 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
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126 MAFF-Net(DAF-Pillar) 75.04 % 85.52 % 67.61 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
127 PBASN code 75.02 % 83.16 % 69.72 % NA s GPU @ 2.5 Ghz (Python)
128 PI-RCNN 74.82 % 84.37 % 70.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
129 EPENet 74.72 % 85.19 % 70.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
130 Pointpillar_TV 74.55 % 83.08 % 69.13 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
131 CentrNet-FG 74.47 % 83.67 % 69.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
132 RethinkDet3D 74.35 % 82.81 % 67.90 % 0.15 s 1 core @ 2.5 Ghz (Python)
133 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.
134 Bit 74.30 % 82.67 % 68.73 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
135 Prune 74.28 % 85.03 % 67.16 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
136 autoRUC 74.08 % 84.54 % 67.04 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
137 Simple3D Net 74.06 % 83.06 % 69.17 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
138 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.
139 PointPiallars_SECA 73.99 % 82.62 % 69.98 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
140 tt code 73.92 % 84.14 % 69.15 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
141 autonet 73.83 % 82.66 % 67.93 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
142 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.
143 C-GCN 73.62 % 83.49 % 67.01 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
144 baseline 73.55 % 82.92 % 67.42 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
145 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.
146 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.
147 TBD 73.02 % 82.74 % 67.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
148 DPointNet 73.02 % 79.25 % 68.53 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
149 PFF3D
This method makes use of Velodyne laser scans.
72.93 % 81.11 % 67.24 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
150 APL-Second 72.87 % 82.75 % 67.91 % 0.05 s 1 core @ 2.5 Ghz (Python)
151 DASS 72.31 % 81.85 % 65.99 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. 2020.
152 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.
153 PointPainting
This method makes use of Velodyne laser scans.
71.70 % 82.11 % 67.08 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
154 RUC code 71.40 % 80.98 % 65.98 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
155 RUC code 71.32 % 81.07 % 64.69 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
156 WS3D
This method makes use of Velodyne laser scans.
70.59 % 80.99 % 64.23 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
157 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.
158 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.
159 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.
160 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.
161 seivl 66.40 % 77.00 % 63.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
162 DAMNET code 65.52 % 76.25 % 59.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
163 voxelrcnn 64.77 % 73.60 % 60.05 % 15 s 1 core @ 2.5 Ghz (C/C++)
164 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.
165 RCD 60.56 % 70.54 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
166 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.
167 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 54.88 % 68.38 % 49.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
168 tiny-stereo-v1
This method uses stereo information.
54.84 % 76.54 % 47.93 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
169 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.
170 CDN
This method uses stereo information.
54.22 % 74.52 % 46.36 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
171 tiny-stereo-v2
This method uses stereo information.
54.18 % 75.05 % 47.16 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
172 CG-Stereo
This method uses stereo information.
53.58 % 74.39 % 46.50 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
173 DSGN
This method uses stereo information.
code 52.18 % 73.50 % 45.14 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
174 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
51.92 % 58.88 % 44.59 % 0.5 s 1 core @ 2.5 Ghz (Python)
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175 BirdNet+
This method makes use of Velodyne laser scans.
code 51.85 % 70.14 % 50.03 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
176 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.
177 CDN-PL++
This method uses stereo information.
44.86 % 64.31 % 38.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. arXiv preprint arXiv:2007.03085 2020.
178 OSE
This method uses stereo information.
43.27 % 64.78 % 37.13 % 0.1 s GPU @ 2.5 Ghz (C/C++)
179 Pseudo-LiDAR++
This method uses stereo information.
code 42.43 % 61.11 % 36.99 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
180 Stereo3D
This method uses stereo information.
41.25 % 65.68 % 30.42 % 0.1 s GPU 1080Ti
181 Disp R-CNN (velo)
This method uses stereo information.
code 39.36 % 59.61 % 32.01 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
182 ZoomNet
This method uses stereo information.
code 38.64 % 55.98 % 30.97 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
183 Disp R-CNN
This method uses stereo information.
code 37.93 % 58.55 % 31.95 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
184 OC Stereo
This method uses stereo information.
code 37.60 % 55.15 % 30.25 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
185 RTS3D 37.38 % 58.51 % 31.12 % 0.03 s GPU @ 2.5 Ghz (Python)
186 Pseudo-Lidar
This method uses stereo information.
code 34.05 % 54.53 % 28.25 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
187 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.
188 SC(DLA34)
This method uses stereo information.
29.53 % 44.40 % 24.96 % 0.05 s GPU @ 2.5 Ghz (Python)
189 IDA-3D
This method uses stereo information.
29.32 % 45.09 % 23.13 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
190 BirdNet
This method makes use of Velodyne laser scans.
27.26 % 40.99 % 25.32 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
191 RT3D-GMP
This method uses stereo information.
23.83 % 32.44 % 17.91 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
192 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.
193 ASOD 22.37 % 38.42 % 17.01 % 0.28 s GPU @ 2.5 Ghz (Python)
194 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.
195 StereoFENet
This method uses stereo information.
18.41 % 29.14 % 14.20 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
196 ITS-MDPL 14.21 % 23.81 % 12.11 % 0.16 s GPU @ 2.5 Ghz (Python)
197 MonoFlex 13.89 % 19.94 % 12.07 % 0.03 s GPU @ 2.5 Ghz (Python)
198 MonoEF code 13.87 % 21.29 % 11.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
199 PSMD 13.57 % 21.37 % 10.89 % 0.1 s GPU @ 2.5 Ghz (Python)
200 CaDDN 13.41 % 19.17 % 11.46 % 0.63 s GPU @ 2.5 Ghz (Python)
201 deprecated 13.30 % 14.81 % 11.04 % 1 core @ 2.5 Ghz (C/C++)
202 Det3D 13.26 % 24.00 % 9.94 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
203 DDMP-3D 12.78 % 19.71 % 9.80 % 0.18 s 1 core @ 2.5 Ghz (Python)
204 S3D 12.75 % 14.58 % 10.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
205 Kinematic3D code 12.72 % 19.07 % 9.17 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
206 DAMono3D 12.66 % 16.99 % 9.97 % 0.09s 1 core @ 2.5 Ghz (C/C++)
207 Object Transformer 12.58 % 17.87 % 10.87 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
208 MTMono3d 12.44 % 18.54 % 10.09 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
209 MonoRUn 12.30 % 19.65 % 10.58 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
210 Deprecated 12.30 % 16.48 % 9.14 % Deprecated Deprecated
211 DLE 12.26 % 17.23 % 10.29 % 0.04 s GPU @ 2.5 Ghz (Python)
212 DP3D 12.24 % 18.84 % 8.96 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
213 YoloMono3D 12.06 % 18.28 % 8.42 % 0.05 s GPU @ 2.5 Ghz (Python)
214 IAFA 12.01 % 17.81 % 10.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
215 D4LCN code 11.72 % 16.65 % 9.51 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
216 GA-Aug 11.67 % 17.46 % 9.69 % 0.04 s GPU @ 2.5 Ghz (Python)
217 MP-Mono 11.65 % 16.78 % 9.01 % 0.16 s GPU @ 2.5 Ghz (Python)
218 MCA 11.63 % 18.46 % 10.24 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
219 PG-MonoNet 11.51 % 15.91 % 9.01 % 0.19 s GPU @ 2.5 Ghz (Python)
220 DA-3Ddet 11.50 % 16.77 % 8.93 % 0.4 s GPU @ 2.5 Ghz (Python)
221 NL_M3D 11.46 % 17.54 % 8.98 % 0.2 s 1 core @ 2.5 Ghz (Python)
222 SSL-RTM3D 11.45 % 16.73 % 9.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
223 IMA 11.34 % 16.24 % 9.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
224 CDI3D 11.32 % 15.70 % 9.26 % 0.03 s GPU @ 2.5 Ghz (Python)
225 LAPNet 11.29 % 18.02 % 8.50 % 0.03 s 1 core @ 2.5 Ghz (Python)
226 DP3D 11.22 % 17.27 % 8.54 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
227 LNET 11.21 % 12.79 % 9.94 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
228 RefinedMPL 11.14 % 18.09 % 8.94 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
229 UM3D_TUM 11.13 % 15.30 % 9.31 % 0.05 s 1 core @ 2.5 Ghz (Python)
230 PatchNet code 11.12 % 15.68 % 10.17 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
231 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.
232 OCM3D 10.44 % 17.48 % 7.87 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
233 RTM3D code 10.34 % 14.41 % 8.77 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
234 MA 10.21 % 14.90 % 8.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
235 MonoPair 9.99 % 13.04 % 8.65 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
236 FADNet code 9.92 % 16.37 % 8.05 % 0.04 s GPU @ >3.5 Ghz (Python)
237 SMOKE code 9.76 % 14.03 % 7.84 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
238 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 .
239 SS3D_HW 9.70 % 14.74 % 7.22 % 0.4 s GPU @ 2.5 Ghz (Python)
240 Center3D 9.31 % 12.01 % 8.06 % 0.05 s GPU @ 3.5 Ghz (Python)
241 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.
242 Mono3CN 9.17 % 12.73 % 7.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
243 LCD3D 9.04 % 13.77 % 7.23 % 0.03 s GPU @ 2.5 Ghz (Python)
244 RAR-Net 8.73 % 13.70 % 6.92 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
245 SSL-RTM3D Res18 8.39 % 12.65 % 7.12 % 0.02 s GPU @ 2.5 Ghz (Python)
246 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.
247 anonymous 7.66 % 15.21 % 6.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
248 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.
249 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.
250 Decoupled-3D 7.02 % 11.08 % 5.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
251 anonymous 6.77 % 13.18 % 5.63 % 1 s 1 core @ 2.5 Ghz (C/C++)
252 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.
253 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.
254 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.
255 MonoFENet 5.14 % 8.35 % 4.10 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
256 TLNet (Stereo)
This method uses stereo information.
code 4.37 % 7.64 % 3.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
257 AACL 4.18 % 5.62 % 3.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
258 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.
259 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.
260 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
261 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.
262 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.
263 3D-GCK 2.52 % 3.27 % 2.11 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
264 SparVox3D 2.49 % 3.73 % 2.09 % 0.05 s GPU @ 2.0 Ghz (Python)
265 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.
266 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.
267 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.
268 UDI-mono3D 0.72 % 0.62 % 0.53 % 0.05 s 1 core @ 2.5 Ghz (Python)
269 UDI-mono3D 0.41 % 0.51 % 0.43 % 0.05 s 1 core @ 2.5 Ghz (Python)
270 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
271 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 HRI-ADLab-HZ-AFree 46.88 % 52.75 % 43.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 HotSpotNet 45.37 % 53.10 % 41.47 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
3 Noah CV Lab - SSL 45.23 % 52.85 % 41.28 % 0.1 s GPU @ 2.5 Ghz (Python)
4 VICNet 44.80 % 54.00 % 41.11 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
5 HRI-ADLab-HZ 44.78 % 52.09 % 42.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 TANet code 44.34 % 53.72 % 40.49 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
7 3DSSD code 44.27 % 54.64 % 40.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
8 PPBA 44.08 % 52.65 % 41.54 % NA s GPU @ 2.5 Ghz (Python)
9 CentrNet-FG 44.02 % 53.51 % 40.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
10 Point-GNN
This method makes use of Velodyne laser scans.
code 43.77 % 51.92 % 40.14 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
11 PP-3D 43.77 % 51.92 % 40.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
12 MVX-Net++ 43.73 % 50.90 % 39.96 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
13 KNN-GCNN 43.57 % 51.82 % 40.02 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
14 F-ConvNet
This method makes use of Velodyne laser scans.
code 43.38 % 52.16 % 38.80 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
15 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 43.35 % 53.10 % 40.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
16 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 43.29 % 52.17 % 40.29 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
17 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.
18 RethinkDet3D 43.25 % 53.13 % 40.58 % 0.15 s 1 core @ 2.5 Ghz (Python)
19 STD code 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.
20 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.
21 SemanticVoxels 42.19 % 50.90 % 39.52 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
22 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.
23 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.
24 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.
25 TBU 41.16 % 49.33 % 38.84 % NA s GPU @ 2.5 Ghz (Python)
26 PointPainting
This method makes use of Velodyne laser scans.
40.97 % 50.32 % 37.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
27 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
40.89 % 46.97 % 38.80 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
28 XView-PartA^2 40.71 % 47.73 % 38.47 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
29 Simple3D Net 40.20 % 48.41 % 37.50 % 0.02 s GPU @ 2.5 Ghz (Python)
30 Fast VP-RCNN code 40.15 % 45.85 % 37.89 % 0.05 s 1 core @ >3.5 Ghz (python)
31 SVGA-Net
This method makes use of Velodyne laser scans.
39.88 % 47.59 % 37.57 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
32 PF-GAP 39.53 % 47.63 % 36.44 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
33 GAP-soft-filter 39.47 % 46.93 % 36.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
34 CVRS VIC-RCNN 39.46 % 45.19 % 37.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
35 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
39.43 % 47.30 % 36.99 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
36 Baseline of CA RCNN 39.42 % 47.30 % 36.97 % 0.1 s GPU @ 2.5 Ghz (Python)
37 CVIS-DF3D 39.42 % 47.30 % 36.97 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
38 FSA-PVRCNN
This method makes use of Velodyne laser scans.
39.39 % 44.14 % 37.13 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
39 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.
40 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.
41 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.
42 MSL3D 38.58 % 45.00 % 35.72 % 0.03 s GPU @ 2.5 Ghz (Python)
43 CVIS-DF3D_v2 38.31 % 45.10 % 36.15 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
44 AF_MCLS 38.29 % 47.07 % 34.67 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
45 3DBN_2 38.23 % 46.79 % 35.57 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
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46 IGRP+ 38.05 % 46.26 % 34.53 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
47 MGACNet 37.50 % 43.55 % 35.33 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
48 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.
49 TBD 37.37 % 43.60 % 34.36 % 0.05 s GPU @ 2.5 Ghz (Python)
50 CVRS VIC-Net 37.18 % 43.82 % 35.35 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
51 PFF3D
This method makes use of Velodyne laser scans.
36.07 % 43.93 % 32.86 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
52 NLK-3D 35.86 % 45.17 % 32.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
53 deprecated 35.21 % 41.32 % 33.32 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
54 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.
55 PBASN code 34.48 % 41.28 % 32.24 % NA s GPU @ 2.5 Ghz (Python)
56 NLK-ALL code 34.46 % 44.30 % 30.83 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
57 DAMNET code 33.66 % 43.32 % 30.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
58 CBi-GNN-persons 32.92 % 41.65 % 29.19 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
59 BirdNet+
This method makes use of Velodyne laser scans.
code 31.46 % 37.99 % 29.46 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
60 Pointpillar_TV 30.79 % 38.56 % 28.57 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
61 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.
62 FCY
This method makes use of Velodyne laser scans.
29.38 % 37.28 % 26.19 % 0.02 s GPU @ 2.5 Ghz (Python)
63 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.
64 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.
65 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.
66 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
24.84 % 31.61 % 21.96 % 0.5 s 1 core @ 2.5 Ghz (Python)
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67 CG-Stereo
This method uses stereo information.
24.31 % 33.22 % 20.95 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
68 Disp R-CNN (velo)
This method uses stereo information.
code 21.98 % 30.98 % 18.68 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
69 Disp R-CNN
This method uses stereo information.
code 21.98 % 31.05 % 18.67 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
70 OSE
This method uses stereo information.
20.65 % 28.68 % 17.65 % 0.1 s GPU @ 2.5 Ghz (C/C++)
71 Stereo3D
This method uses stereo information.
19.75 % 28.49 % 16.48 % 0.1 s GPU 1080Ti
72 OC Stereo
This method uses stereo information.
code 17.58 % 24.48 % 15.60 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
73 BirdNet
This method makes use of Velodyne laser scans.
17.08 % 22.04 % 15.82 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
74 DSGN
This method uses stereo information.
code 15.55 % 20.53 % 14.15 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
75 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.
76 CaDDN 8.14 % 12.87 % 6.76 % 0.63 s GPU @ 2.5 Ghz (Python)
77 RefinedMPL 7.18 % 11.14 % 5.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
78 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.
79 MonoRUn 6.78 % 10.88 % 5.83 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
80 MonoPair 6.68 % 10.02 % 5.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
81 DLE 6.55 % 9.64 % 5.44 % 0.04 s GPU @ 2.5 Ghz (Python)
82 MonoFlex 6.31 % 9.43 % 5.26 % 0.03 s GPU @ 2.5 Ghz (Python)
83 DAMono3D 5.68 % 7.86 % 4.81 % 0.09s 1 core @ 2.5 Ghz (C/C++)
84 Deprecated 5.62 % 7.52 % 4.71 % Deprecated Deprecated
85 SS3D_HW 5.00 % 7.77 % 4.03 % 0.4 s GPU @ 2.5 Ghz (Python)
86 GA-Aug 4.89 % 8.04 % 4.32 % 0.04 s GPU @ 2.5 Ghz (Python)
87 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.
88 PG-MonoNet 4.50 % 5.76 % 3.93 % 0.19 s GPU @ 2.5 Ghz (Python)
89 M3D-RPN(S-R) 4.11 % 5.70 % 3.37 % 0.16 s GPU @ 1.5 Ghz (Python)
90 CDI3D 4.03 % 5.64 % 3.29 % 0.03 s GPU @ 2.5 Ghz (Python)
91 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.
92 NL_M3D 3.87 % 5.16 % 3.08 % 0.2 s 1 core @ 2.5 Ghz (Python)
93 MP-Mono 3.79 % 5.30 % 3.15 % 0.16 s GPU @ 2.5 Ghz (Python)
94 DDMP-3D 3.55 % 4.93 % 3.01 % 0.18 s 1 core @ 2.5 Ghz (Python)
95 DP3D 3.54 % 4.75 % 2.88 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
96 FADNet code 3.53 % 5.40 % 3.31 % 0.04 s GPU @ >3.5 Ghz (Python)
97 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 .
98 Mono3CN 3.44 % 5.13 % 3.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
99 Center3D 3.43 % 4.86 % 2.78 % 0.05 s GPU @ 3.5 Ghz (Python)
100 RT3D-GMP
This method uses stereo information.
3.42 % 4.51 % 2.77 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
101 D4LCN code 3.42 % 4.55 % 2.83 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
102 DP3D 3.37 % 4.77 % 2.77 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
103 LAPNet 3.16 % 4.41 % 2.70 % 0.03 s 1 core @ 2.5 Ghz (Python)
104 MonoEF code 2.79 % 4.27 % 2.21 % 0.03 s 1 core @ 2.5 Ghz (Python)
105 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.
106 MTMono3d 2.05 % 2.40 % 1.68 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
107 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.
108 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.
109 UM3D_TUM 1.74 % 3.49 % 1.74 % 0.05 s 1 core @ 2.5 Ghz (Python)
110 UDI-mono3D 1.45 % 2.18 % 1.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
111 UDI-mono3D 1.01 % 1.81 % 0.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
112 SparVox3D 0.25 % 0.35 % 0.25 % 0.05 s GPU @ 2.0 Ghz (Python)
113 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
114 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 HRI-MSP-L
This method makes use of Velodyne laser scans.
71.86 % 87.77 % 63.57 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
2 Noah CV Lab - SSL 71.53 % 84.24 % 62.20 % 0.1 s GPU @ 2.5 Ghz (Python)
3 HRI-ADLab-HZ-AFree 69.92 % 84.65 % 63.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 Fast VP-RCNN code 69.24 % 85.43 % 62.10 % 0.05 s 1 core @ >3.5 Ghz (python)
5 HRI-ADLab-HZ 68.83 % 84.82 % 60.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
68.54 % 82.19 % 61.33 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
7 RangeIoUDet
This method makes use of Velodyne laser scans.
67.77 % 83.12 % 60.26 % 0.02 s 1 core @ 2.5 Ghz (Python)
8 PV-RCNN-v2 67.33 % 82.22 % 60.04 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
9 CBi-GNN-persons 66.49 % 79.95 % 59.12 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
10 XView-PartA^2 66.33 % 80.65 % 59.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
11 HotSpotNet 65.95 % 82.59 % 59.00 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
12 TBD 65.64 % 82.29 % 57.98 % 0.05 s GPU @ 2.5 Ghz (Python)
13 FSA-PVRCNN
This method makes use of Velodyne laser scans.
65.20 % 80.68 % 59.14 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
14 F-ConvNet
This method makes use of Velodyne laser scans.
code 65.07 % 81.98 % 56.54 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
15 CVRS VIC-RCNN 64.99 % 81.47 % 58.62 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
16 3DSSD code 64.10 % 82.48 % 56.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
17 PointPainting
This method makes use of Velodyne laser scans.
63.78 % 77.63 % 55.89 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
18 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 63.71 % 78.60 % 57.65 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
19 CVRS VIC-Net 63.65 % 78.29 % 57.27 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
20 NLK-ALL code 63.65 % 79.94 % 57.28 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
21 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 63.52 % 79.17 % 56.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
22 Point-GNN
This method makes use of Velodyne laser scans.
code 63.48 % 78.60 % 57.08 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
23 PP-3D 63.48 % 78.60 % 57.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
24 CVIS-DF3D_v2 63.05 % 77.46 % 55.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
25 KNN-GCNN 62.91 % 80.24 % 56.49 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
26 MSL3D 62.27 % 76.74 % 56.20 % 0.03 s GPU @ 2.5 Ghz (Python)
27 deprecated 62.16 % 75.45 % 56.00 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
28 GAP-soft-filter 62.04 % 77.06 % 55.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
29 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
62.02 % 77.35 % 55.52 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
30 Baseline of CA RCNN 62.02 % 77.33 % 55.52 % 0.1 s GPU @ 2.5 Ghz (Python)
31 CVIS-DF3D 62.02 % 77.33 % 55.52 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
32 MGACNet 62.00 % 78.73 % 55.18 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
33 PPBA 61.99 % 79.42 % 55.34 % NA s GPU @ 2.5 Ghz (Python)
34 TBU 61.99 % 79.42 % 55.34 % NA s GPU @ 2.5 Ghz (Python)
35 SVGA-Net
This method makes use of Velodyne laser scans.
61.86 % 75.45 % 54.68 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
36 STD code 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.
37 RethinkDet3D 61.10 % 79.31 % 54.47 % 0.15 s 1 core @ 2.5 Ghz (Python)
38 MVX-Net++ 61.03 % 76.07 % 53.41 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
39 AF_MCLS 60.89 % 78.82 % 54.13 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
40 3DBN_2 60.88 % 78.10 % 54.10 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
41 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.
42 CCFNET 60.18 % 78.05 % 53.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
43 VICNet 59.99 % 78.75 % 52.37 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
44 PF-GAP 59.92 % 77.88 % 53.48 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
45 FCY
This method makes use of Velodyne laser scans.
59.54 % 76.30 % 52.29 % 0.02 s GPU @ 2.5 Ghz (Python)
46 TANet code 59.44 % 75.70 % 52.53 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
47 PBASN code 59.43 % 76.80 % 52.77 % NA s GPU @ 2.5 Ghz (Python)
48 NLK-3D 59.30 % 76.45 % 51.82 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
49 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.
50 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.
51 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.
52 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.
53 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.
54 CentrNet-FG 55.54 % 72.07 % 49.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
55 Pointpillar_TV 54.69 % 71.61 % 48.22 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
56 Simple3D Net 54.49 % 70.79 % 48.21 % 0.02 s GPU @ 2.5 Ghz (Python)
57 IGRP+ 53.22 % 69.87 % 47.55 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
58 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.
59 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.
60 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.
61 BirdNet+
This method makes use of Velodyne laser scans.
code 47.72 % 67.38 % 42.89 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
62 PFF3D
This method makes use of Velodyne laser scans.
46.78 % 63.27 % 41.37 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
63 DAMNET code 42.82 % 58.71 % 38.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
64 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.
65 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.
66 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.
67 CG-Stereo
This method uses stereo information.
30.89 % 47.40 % 27.23 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
68 BirdNet
This method makes use of Velodyne laser scans.
30.25 % 43.98 % 27.21 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
69 Disp R-CNN (velo)
This method uses stereo information.
code 23.75 % 39.72 % 20.47 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
70 Disp R-CNN
This method uses stereo information.
code 23.75 % 39.72 % 20.47 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
71 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.
72 DSGN
This method uses stereo information.
code 18.17 % 27.76 % 16.21 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
73 OSE
This method uses stereo information.
17.28 % 28.50 % 15.56 % 0.1 s GPU @ 2.5 Ghz (C/C++)
74 OC Stereo
This method uses stereo information.
code 16.63 % 29.40 % 14.72 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
75 RT3D-GMP
This method uses stereo information.
4.90 % 7.75 % 4.18 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
76 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.
77 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.
78 DAMono3D 3.80 % 5.65 % 3.23 % 0.09s 1 core @ 2.5 Ghz (C/C++)
79 CaDDN 3.41 % 7.00 % 3.30 % 0.63 s GPU @ 2.5 Ghz (Python)
80 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.
81 Deprecated 2.71 % 3.89 % 2.27 % Deprecated Deprecated
82 CDI3D 2.69 % 4.15 % 2.45 % 0.03 s GPU @ 2.5 Ghz (Python)
83 DLE 2.66 % 4.59 % 2.45 % 0.04 s GPU @ 2.5 Ghz (Python)
84 DDMP-3D 2.50 % 4.18 % 2.32 % 0.18 s 1 core @ 2.5 Ghz (Python)
85 Center3D 2.35 % 4.32 % 2.06 % 0.05 s GPU @ 3.5 Ghz (Python)
86 MonoFlex 2.35 % 4.17 % 2.04 % 0.03 s GPU @ 2.5 Ghz (Python)
87 Mono3CN 2.17 % 3.68 % 2.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 SS3D_HW 2.17 % 4.29 % 2.00 % 0.4 s GPU @ 2.5 Ghz (Python)
89 MonoPair 2.12 % 3.79 % 1.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
90 RefinedMPL 1.82 % 3.23 % 1.77 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
91 UDI-mono3D 1.74 % 3.29 % 1.37 % 0.05 s 1 core @ 2.5 Ghz (Python)
92 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.
93 D4LCN code 1.67 % 2.45 % 1.36 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
94 DP3D 1.66 % 2.77 % 1.31 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
95 NL_M3D 1.51 % 2.10 % 1.58 % 0.2 s 1 core @ 2.5 Ghz (Python)
96 UDI-mono3D 1.47 % 3.01 % 1.47 % 0.05 s 1 core @ 2.5 Ghz (Python)
97 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.
98 PG-MonoNet 1.43 % 2.41 % 1.23 % 0.19 s GPU @ 2.5 Ghz (Python)
99 MP-Mono 1.42 % 1.89 % 1.29 % 0.16 s GPU @ 2.5 Ghz (Python)
100 DP3D 1.39 % 2.04 % 1.12 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
101 GA-Aug 1.20 % 1.99 % 1.09 % 0.04 s GPU @ 2.5 Ghz (Python)
102 MonoEF code 0.92 % 1.80 % 0.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
103 MTMono3d 0.90 % 1.59 % 0.96 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
104 LAPNet 0.89 % 1.37 % 0.62 % 0.03 s 1 core @ 2.5 Ghz (Python)
105 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 .
106 FADNet code 0.64 % 1.44 % 0.67 % 0.04 s GPU @ >3.5 Ghz (Python)
107 UM3D_TUM 0.62 % 0.45 % 0.62 % 0.05 s 1 core @ 2.5 Ghz (Python)
108 MonoRUn 0.61 % 1.01 % 0.48 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
109 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.
110 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
111 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

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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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