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 SE-SSD
This method makes use of Velodyne laser scans.
91.84 % 95.68 % 86.72 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
2 ADLAB 91.66 % 95.56 % 86.92 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
3 SPANet 91.59 % 95.59 % 86.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
4 PVGNet 91.26 % 94.36 % 86.63 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
5 SA-SSD code 91.03 % 95.03 % 85.96 % 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.
6 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 90.65 % 94.98 % 86.14 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
7 CN 90.50 % 94.51 % 85.86 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
8 HyBrid Feature Det 90.35 % 92.87 % 85.87 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
9 Fast VP-RCNN code 90.32 % 95.09 % 85.84 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
10 LZY_RCNN 90.29 % 92.88 % 85.84 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
11 E^2-PV-RCNN 90.27 % 92.51 % 86.01 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
12 DomainAdp+PVRCNN
This method makes use of Velodyne laser scans.
90.23 % 92.73 % 86.09 % 0.09 s GPU @ 2.5 Ghz (Python)
13 anonymous code 90.22 % 94.86 % 85.73 % 0.05s 1 core @ >3.5 Ghz (python)
14 MSG-PGNN 90.20 % 92.89 % 85.80 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
15 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
90.13 % 92.42 % 85.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
16 XView 90.12 % 92.27 % 85.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 FPC-RCNN 90.03 % 92.74 % 85.67 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
18 Associate-3Ddet_v2 90.00 % 95.55 % 84.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
19 SAA-PV-RCNN 89.88 % 91.54 % 86.93 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
20 FSA-PVRCNN
This method makes use of Velodyne laser scans.
89.87 % 92.30 % 85.71 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
21 EBM3DOD code 89.86 % 95.64 % 84.56 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
22 CIA-SSD
This method makes use of Velodyne laser scans.
code 89.84 % 93.74 % 82.39 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
23 HIKVISION-ADLab-HZ 89.83 % 93.21 % 84.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
24 AIMC-RUC 89.80 % 93.64 % 84.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
25 CLOCs_PVCas code 89.80 % 93.05 % 86.57 % 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.
26 CIA-SSD v2
This method makes use of Velodyne laser scans.
89.80 % 93.49 % 84.39 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
27 deprecated 89.77 % 93.68 % 82.31 % deprecated deprecated
28 CM3DV 89.77 % 95.54 % 84.49 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
29 EA-M-RCNN(BorderAtt) 89.76 % 94.67 % 86.73 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
30 AM-SSD 89.74 % 95.56 % 84.65 % 0.04 s 1 core @ 2.5 Ghz (Python)
31 CBi-GNN 89.74 % 95.92 % 84.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
32 OAP 89.72 % 93.13 % 82.25 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
33 D3D 89.72 % 93.37 % 84.72 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
34 EBM3DOD baseline code 89.63 % 95.44 % 84.34 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
35 3D-CVF at SPA
This method makes use of Velodyne laser scans.
89.56 % 93.52 % 82.45 % 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.
36 CDE-Net(0.3) 89.54 % 95.26 % 82.31 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Multi sensor fusion 3d object detection method. Submitted to OSA 2021.
37 Cas-SSD 89.47 % 93.31 % 84.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 FCY
This method makes use of Velodyne laser scans.
89.46 % 95.27 % 84.34 % 0.02 s GPU @ 2.5 Ghz (Python)
39 PointRes
This method makes use of Velodyne laser scans.
89.42 % 93.17 % 84.25 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
40 Seg-RCNN code 89.39 % 93.36 % 81.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
41 HUAWEI Octopus 89.39 % 92.58 % 86.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 CDE-Net(0.4) 89.38 % 94.91 % 84.29 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Multi sensor fusion 3d object detection method. Submitted to OSA 2021.
43 PLNL-3DSSD
This method makes use of Velodyne laser scans.
89.36 % 93.00 % 84.18 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
44 PSS 89.28 % 93.17 % 84.38 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
45 GNN-RCNN 89.28 % 92.13 % 85.49 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
46 CJJ 89.20 % 92.90 % 84.30 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
47 STD code 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.
48 Point-GNN
This method makes use of Velodyne laser scans.
code 89.17 % 93.11 % 83.90 % 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.
49 PP-3D 89.17 % 93.11 % 83.90 % 0.1 s 1 core @ 2.5 Ghz (Python)
50 RoIFusion code 89.03 % 92.88 % 83.94 % 0.22 s 1 core @ 3.0 Ghz (Python)
51 3DSSD code 89.02 % 92.66 % 85.86 % 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.
52 CityBrainLab-TSD 88.91 % 92.78 % 84.12 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
53 NLK-ALL code 88.89 % 92.25 % 84.13 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
54 H^23D R-CNN 88.87 % 92.85 % 86.07 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
55 TBD 88.83 % 92.96 % 86.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
56 Voxel R-CNN code 88.83 % 94.85 % 86.13 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . arXiv preprint arXiv:2012.15712 2020.
57 HVNet 88.82 % 92.83 % 83.38 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
58 RangeRCNN-LV 88.81 % 92.41 % 85.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 F-3DNet 88.76 % 92.68 % 83.63 % 0.5 s GPU @ 2.5 Ghz (Python)
60 PV-RCNN-v2 88.74 % 92.66 % 85.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
61 3DIoU+++ 88.61 % 92.23 % 86.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 RangeIoUDet
This method makes use of Velodyne laser scans.
88.59 % 92.28 % 85.83 % 0.02 s 1 core @ 2.5 Ghz (Python)
63 SIEV-Net 88.58 % 92.27 % 83.36 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
64 ISF-v2 88.57 % 92.15 % 83.91 % 0.04 s 1 core @ 2.5 Ghz (Python)
65 KNN-GCNN 88.57 % 91.73 % 83.32 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
66 PVF-NET 88.57 % 92.20 % 83.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
67 CVRS VIC-Net 88.57 % 91.94 % 85.43 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
68 BLPNet_V2 88.55 % 92.24 % 83.44 % 0.04 s 1 core @ 2.5 Ghz (Python)
69 nonet 88.49 % 91.97 % 85.33 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
70 EPNet code 88.47 % 94.22 % 83.69 % 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.
71 CenterNet3D 88.46 % 91.80 % 83.62 % 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.
72 deprecated 88.44 % 92.14 % 85.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
73 SIENet 88.44 % 92.00 % 85.90 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
74 PC-RGNN 88.43 % 92.08 % 85.81 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
75 RangeRCNN
This method makes use of Velodyne laser scans.
88.40 % 92.15 % 85.74 % 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.
76 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.
77 3D IoU-Net 88.38 % 94.76 % 81.93 % 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.
78 PF-GAP 88.35 % 92.16 % 85.64 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
79 ReFineNet 88.32 % 91.93 % 85.68 % 0.08 s 1 core @ 2.5 Ghz (Python)
80 CLOCs_SecCas 88.23 % 91.16 % 82.63 % 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.
81 MSL3D 88.23 % 91.64 % 85.53 % 0.03 s GPU @ 2.5 Ghz (Python)
82 Multi-Sensor3D 88.23 % 91.64 % 85.53 % 0.03 s GPU @ 2.5 Ghz (Python)
83 3DIoU_v2 88.22 % 92.52 % 85.90 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
84 NLK-3D 88.22 % 91.54 % 83.33 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
85 CVRS_PF 88.22 % 91.81 % 84.91 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
86 SVGA-Net
This method makes use of Velodyne laser scans.
88.21 % 91.98 % 85.46 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
87 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.
88 CVRS VIC-RCNN 88.20 % 92.35 % 85.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
89 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.
90 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
88.17 % 92.01 % 85.43 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
91 3DIoU++ 88.16 % 91.79 % 85.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 FPC3D
This method makes use of the epipolar geometry.
88.15 % 91.92 % 85.32 % 33 s 1 core @ 2.5 Ghz (C/C++)
93 Baseline of CA RCNN 88.13 % 91.91 % 85.40 % 0.1 s GPU @ 2.5 Ghz (Python)
94 CVIS-DF3D 88.13 % 91.91 % 85.40 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
95 FPCR-CNN 88.12 % 92.62 % 85.18 % 0.05 s 1 core @ 2.5 Ghz (Python)
96 GAP-soft-filter 88.11 % 91.88 % 85.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
97 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. CVPR 2020.
98 SERCNN
This method makes use of Velodyne laser scans.
88.10 % 94.11 % 83.43 % 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.
99 Associate-3Ddet code 88.09 % 91.40 % 82.96 % 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.
100 HotSpotNet 88.09 % 94.06 % 83.24 % 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.
101 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 88.08 % 91.90 % 85.35 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
102 CVIS-DF3D_v2 88.06 % 91.85 % 85.37 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
103 Dccnet 88.01 % 92.09 % 82.45 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
104 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.
105 CCFNET 87.97 % 94.25 % 83.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
106 HV 87.94 % 91.76 % 83.03 % 0.02 s GPU @ 2.5 Ghz (Python)
107 AIMC-RUC 87.91 % 93.92 % 82.70 % 0.11 s 1 core @ 2.5 Ghz (Python)
108 TBD 87.89 % 91.39 % 85.24 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
109 tbd code 87.88 % 91.36 % 84.75 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
110 XView-PartA^2 87.84 % 91.94 % 85.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
111 Fast Point R-CNN
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.
112 TBD 87.83 % 91.80 % 85.19 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
113 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 87.79 % 91.70 % 84.61 % 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.
114 MKFFNet 87.78 % 91.85 % 84.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
115 FPGNN 87.78 % 92.21 % 80.86 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
116 HRI-MSP-L
This method makes use of Velodyne laser scans.
87.78 % 91.74 % 85.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
117 SIF 87.76 % 91.44 % 85.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
118 MVAF-Net code 87.73 % 91.95 % 85.00 % 0.06 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. arXiv preprint arXiv:2011.00652 2020.
119 MGACNet 87.68 % 90.93 % 84.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
120 deprecated 87.63 % 93.66 % 80.35 % 0.06 s GPU @ >3.5 Ghz (Python)
121 VAL 87.63 % 93.57 % 79.89 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
122 MODet
This method makes use of Velodyne laser scans.
87.56 % 90.80 % 82.69 % 0.05 s GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
123 VOXEL_3D 87.55 % 90.83 % 82.24 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
124 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.
125 V3D 87.53 % 90.83 % 82.30 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
126 PointRGCN 87.49 % 91.63 % 80.73 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
127 AF_V1 87.47 % 92.70 % 82.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
128 MKFFNet 87.41 % 91.62 % 84.67 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
129 MKFFNet 87.41 % 91.93 % 84.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
130 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.
131 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.
132 MAFF-Net(DAF-Pillar) 87.34 % 90.79 % 77.66 % 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.
133 VAR 87.31 % 90.68 % 82.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
134 DPointNet 87.29 % 88.96 % 82.61 % 0.07s 1 core @ 2.5 Ghz (C/C++)
135 HRI-VoxelFPN 87.21 % 92.75 % 79.82 % 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.
136 VGCN 87.16 % 90.67 % 82.98 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
137 MDA 87.13 % 90.67 % 82.80 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
138 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.
139 Pointpillar_TV 87.08 % 90.50 % 81.98 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
140 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.
141 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.
142 C-GCN 86.78 % 91.11 % 80.09 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
143 FLID 86.77 % 91.58 % 81.14 % 0.04 s GPU @ 2.5 Ghz (Python)
144 CU-PointRCNN 86.69 % 92.65 % 82.66 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
145 tt code 86.68 % 90.57 % 81.98 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
146 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.
147 TANet code 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.
148 MVX-Net++ 86.53 % 91.86 % 81.41 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
149 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.
150 Simple3D Net 86.46 % 89.82 % 82.60 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
151 SegVoxelNet 86.37 % 91.62 % 83.04 % 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.
152 IGRP+ 86.29 % 92.20 % 81.48 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
153 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.
154 IGRP 86.21 % 92.04 % 81.30 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
155 R-GCN 86.05 % 91.91 % 81.05 % 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.
156 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.
157 TBD 86.00 % 89.79 % 83.37 % 0.05 s GPU @ 2.5 Ghz (Python)
158 IOU-SSD code 85.98 % 90.18 % 80.74 % 0.045s 1 core @ 2.5 Ghz (C/C++)
159 TBD 85.91 % 90.88 % 80.95 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
160 LSNet 85.89 % 92.12 % 80.80 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
161 DASS 85.85 % 91.74 % 80.97 % 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.
162 F-ConvNet
This method makes use of Velodyne laser scans.
code 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.
163 PI-RCNN 85.81 % 91.44 % 81.00 % 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.
164 APL-Second 85.70 % 90.78 % 78.69 % 0.05 s 1 core @ 2.5 Ghz (Python)
165 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.
166 3DBN_2 85.30 % 91.37 % 82.57 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
167 PFF3D
This method makes use of Velodyne laser scans.
85.08 % 89.61 % 80.42 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
168 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.
169 WS3D
This method makes use of Velodyne laser scans.
84.93 % 90.96 % 77.96 % 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.
170 baseline 84.88 % 89.25 % 80.18 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
171 FPC3D_all
This method makes use of Velodyne laser scans.
84.85 % 91.05 % 80.23 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
172 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.
173 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.
174 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.
175 KMC code 83.90 % 88.87 % 76.87 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
176 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.
177 DAMNET code 82.14 % 87.90 % 75.52 % 1 s 1 core @ 2.5 Ghz (C/C++)
178 voxelrcnn 81.41 % 88.21 % 75.26 % 15 s 1 core @ 2.5 Ghz (C/C++)
179 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.
180 NLK 79.15 % 82.59 % 72.65 % 0.02 s 1 core @ 2.5 Ghz (Python)
181 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.
182 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.
183 RCD 75.83 % 82.26 % 69.61 % 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.
184 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.
185 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 73.80 % 84.61 % 65.59 % 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.
186 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.
187 VN3D 70.69 % 80.56 % 65.31 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
188 AEC3D 70.66 % 81.54 % 65.27 % 0.01 s GPU @ 2.5 Ghz (Python)
189 stereo-tkc
This method uses stereo information.
69.70 % 87.15 % 62.51 % 0.4 s GPU @ 2.0 Ghz (Python + C/C++)
190 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.
191 tiny-stereo-v2
This method uses stereo information.
68.87 % 86.89 % 59.95 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
192 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.
193 tiny-stereo
This method uses stereo information.
67.43 % 87.93 % 58.05 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
194 CG-Stereo
This method uses stereo information.
66.44 % 85.29 % 58.95 % 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.
195 PLUME
This method uses stereo information.
66.27 % 82.97 % 56.70 % 0.15 s GPU @ 2.5 Ghz (Python)
196 CDN
This method uses stereo information.
code 66.24 % 83.32 % 57.65 % 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.
197 DSGN
This method uses stereo information.
code 65.05 % 82.90 % 56.60 % 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.
198 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.
199 BirdNet+
This method makes use of Velodyne laser scans.
code 63.33 % 84.80 % 61.23 % 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.
200 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.
201 CDN-PL++
This method uses stereo information.
61.04 % 81.27 % 52.84 % 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. Advances in Neural Information Processing Systems 2020.
202 BirdNet
This method makes use of Velodyne laser scans.
59.83 % 84.17 % 57.35 % 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.
203 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.
204 RT3D-GMP
This method uses stereo information.
59.00 % 69.14 % 45.49 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
205 OSE+ 58.65 % 79.80 % 50.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
206 Disp R-CNN (velo)
This method uses stereo information.
code 58.62 % 79.76 % 47.73 % 0.387 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.
207 SOD 58.50 % 81.25 % 49.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
208 OSE
This method uses stereo information.
58.04 % 79.75 % 49.78 % 0.1 s GPU @ 2.5 Ghz (C/C++)
209 Pseudo-LiDAR++
This method uses stereo information.
code 58.01 % 78.31 % 51.25 % 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.
210 Disp R-CNN
This method uses stereo information.
code 57.98 % 79.61 % 47.09 % 0.387 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.
211 NCL code 57.66 % 50.87 % 57.99 % NA s 1 core @ 2.5 Ghz (Python)
212 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.
213 Neighbor-VoteNet 54.68 % 65.38 % 48.59 % 0.1 s 1 core @ 2.5 Ghz (Python)
214 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.
215 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.
216 RTS3D 51.79 % 72.17 % 43.19 % 0.03 s GPU @ 2.5 Ghz (Python)
217 OC Stereo
This method uses stereo information.
code 51.47 % 68.89 % 42.97 % 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.
218 NVNet(BEV-3D) 50.41 % 61.32 % 44.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
219 Stereo3D
This method uses stereo information.
50.28 % 76.10 % 36.86 % 0.1 s GPU 1080Ti
220 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.
221 Pseudo-Lidar
This method uses stereo information.
code 45.00 % 67.30 % 38.40 % 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.
222 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.
223 SC(DLA34+DCO)
This method uses stereo information.
42.12 % 62.97 % 35.37 % 0.07 s GPU @ 2.5 Ghz (Python)
224 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.
225 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.
226 LNET 29.68 % 34.30 % 25.11 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
227 Det3D 20.80 % 35.46 % 16.00 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
228 LGDet3d 20.17 % 30.72 % 16.76 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
229 ITS-MDPL 19.83 % 33.74 % 16.90 % 0.16 s GPU @ 2.5 Ghz (Python)
230 MonoFlex 19.75 % 28.23 % 16.89 % 0.03 s GPU @ 2.5 Ghz (Python)
231 MonoEF code 19.70 % 29.03 % 17.26 % 0.03 s 1 core @ 2.5 Ghz (Python)
232 CaDDN 18.91 % 27.94 % 17.19 % 0.63 s GPU @ 2.5 Ghz (Python)
233 DLE 18.89 % 24.79 % 16.00 % 0.04 s GPU @ 2.5 Ghz (Python)
234 Object Transformer 18.78 % 26.43 % 15.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
235 PLDet3d 18.55 % 29.14 % 15.73 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
236 MTMono3d 18.54 % 27.00 % 15.71 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
237 MonoGeo 18.42 % 24.40 % 16.03 % 0.05 s 1 core @ 2.5 Ghz (Python)
238 DDMP-3D 17.89 % 28.08 % 13.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
239 IAFA 17.88 % 25.88 % 15.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
240 RelationNet3D 17.66 % 25.56 % 15.52 % 0.04 s GPU @ 2.5 Ghz (Python)
241 RefinedMPL 17.60 % 28.08 % 13.95 % 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.
242 Kinematic3D code 17.52 % 26.69 % 13.10 % 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 .
243 MonoRUn 17.34 % 27.94 % 15.24 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
244 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.
245 Deprecated 17.22 % 23.59 % 13.34 % Deprecated Deprecated
246 DAMono3D 17.17 % 23.73 % 13.46 % 0.09s 1 core @ 2.5 Ghz (C/C++)
247 YoloMono3D code 17.15 % 26.79 % 12.56 % 0.05 s GPU @ 2.5 Ghz (Python)
248 OCM3D 17.13 % 27.87 % 13.53 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
249 IMA 17.08 % 23.93 % 14.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
250 MCA 17.07 % 25.93 % 14.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
251 DP3D 16.96 % 26.51 % 12.82 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
252 TBD 16.93 % 29.02 % 14.58 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
253 PatchNet code 16.86 % 22.97 % 14.97 % 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.
254 RetinaMono code 16.85 % 24.52 % 14.02 % 0.02 s 1 core @ 2.5 Ghz (Python)
255 UM3D_TUM 16.69 % 23.63 % 14.17 % 0.05 s 1 core @ 2.5 Ghz (Python)
256 GA-Aug 16.45 % 24.64 % 14.15 % 0.04 s GPU @ 2.5 Ghz (Python)
257 PG-MonoNet 16.31 % 23.31 % 13.03 % 0.19 s GPU @ 2.5 Ghz (Python)
258 KM3D code 16.20 % 23.44 % 14.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
259 CDI3D 16.06 % 22.06 % 13.43 % 0.03 s GPU @ 2.5 Ghz (Python)
260 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. CVPR 2020.
261 MP-Mono 16.01 % 23.45 % 12.07 % 0.16 s GPU @ 2.5 Ghz (Python)
262 NL_M3D 15.93 % 24.15 % 12.11 % 0.2 s 1 core @ 2.5 Ghz (Python)
263 DA-3Ddet 15.90 % 23.35 % 12.11 % 0.4 s GPU @ 2.5 Ghz (Python)
264 LAPNet 15.76 % 25.10 % 12.30 % 0.03 s 1 core @ 2.5 Ghz (Python)
265 MA 15.43 % 22.01 % 14.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
266 MonoPair 14.83 % 19.28 % 12.89 % 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.
267 Decoupled-3D 14.82 % 23.16 % 11.25 % 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.
268 modat3D
This is an online method (no batch processing).
14.71 % 20.16 % 12.76 % 0.03 s GPU @ 2.5 Ghz (Python)
269 SMOKE code 14.49 % 20.83 % 12.75 % 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.
270 RelationNet3D_res18 14.30 % 19.93 % 12.37 % 0.04 s GPU @ 2.5 Ghz (Python)
271 FADNet code 14.22 % 23.00 % 12.56 % 0.04 s GPU @ >3.5 Ghz (Python)
272 RTM3D code 14.20 % 19.17 % 11.99 % 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.
273 LCD3D 13.99 % 21.97 % 11.43 % 0.03 s GPU @ 2.5 Ghz (Python)
274 Center3D 13.98 % 18.89 % 12.44 % 0.05 s GPU @ 3.5 Ghz (Python)
275 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.
276 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 .
277 SSL-RTM3D Res18 13.37 % 19.71 % 11.10 % 0.02 s GPU @ 2.5 Ghz (Python)
278 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.
279 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.
280 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.
281 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.
282 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.
283 anonymous 10.96 % 20.42 % 9.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
284 anonymous 10.06 % 18.80 % 8.56 % 1 s 1 core @ 2.5 Ghz (C/C++)
285 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.
286 TLNet (Stereo)
This method uses stereo information.
code 7.69 % 13.71 % 6.73 % 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.
287 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.
288 AACL 6.75 % 8.55 % 5.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
289 SparVox3D 6.39 % 10.20 % 5.06 % 0.05 s GPU @ 2.0 Ghz (Python)
290 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.
291 MVRA + I-FRCNN+ 5.84 % 9.05 % 4.50 % 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.
292 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.
293 3D-GCK 4.57 % 5.79 % 3.64 % 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.
294 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.
295 UDI-mono3D 3.08 % 3.93 % 2.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
296 UDI-mono3D 2.79 % 3.38 % 2.37 % 0.05 s 1 core @ 2.5 Ghz (Python)
297 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.
298 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 .
299 GAA 0.00 % 0.00 % 0.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
300 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
301 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.
302 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 ADLAB 52.58 % 58.39 % 49.13 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
2 SIEV-Net 52.15 % 60.78 % 48.54 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
3 TANet code 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 HIKVISION-AFree 50.67 % 56.54 % 48.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 50.57 % 59.86 % 46.74 % 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.
6 HotSpotNet 50.53 % 57.39 % 46.65 % 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.
7 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.
8 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.
9 3DSSD code 49.94 % 60.54 % 45.73 % 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.
10 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. CVPR 2020.
11 SemanticVoxels 49.93 % 58.91 % 47.31 % 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.
12 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 49.81 % 59.04 % 45.92 % 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.
13 HIKVISION-ADLab-HZ 49.62 % 55.94 % 47.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
14 SAA-PV-RCNN 49.58 % 57.07 % 46.49 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
15 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.
16 F-ConvNet
This method makes use of Velodyne laser scans.
code 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.
17 HVNet 48.86 % 54.84 % 46.33 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
18 STD code 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.
19 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.
20 AF 48.46 % 56.09 % 44.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
21 TBD_IOU2 48.15 % 56.54 % 45.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 MVX-Net++ 48.04 % 56.63 % 45.44 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
23 Simple3D Net 47.27 % 56.05 % 44.70 % 0.02 s GPU @ 2.5 Ghz (Python)
24 Point-GNN
This method makes use of Velodyne laser scans.
code 47.07 % 55.36 % 44.61 % 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.
25 PP-3D 47.07 % 55.36 % 44.61 % 0.1 s 1 core @ 2.5 Ghz (Python)
26 KNN-GCNN 46.77 % 55.11 % 44.43 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
27 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.
28 TBD_IOU1 46.59 % 53.92 % 44.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
29 XView-PartA^2 46.57 % 52.45 % 43.85 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
30 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.
31 GNN-RCNN 46.10 % 52.19 % 43.97 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
32 TBD_IOU 46.08 % 53.25 % 43.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
33 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.
34 E^2-PV-RCNN 45.85 % 52.35 % 44.00 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
35 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
45.82 % 52.03 % 43.81 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
36 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.
37 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.
38 FPCR-CNN 45.18 % 52.79 % 42.70 % 0.05 s 1 core @ 2.5 Ghz (Python)
39 PF-GAP 45.02 % 53.73 % 41.88 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
40 GAP-soft-filter 44.98 % 52.44 % 42.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
41 FPC-RCNN 44.96 % 51.54 % 42.88 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
42 Baseline of CA RCNN 44.85 % 52.42 % 42.56 % 0.1 s GPU @ 2.5 Ghz (Python)
43 CVIS-DF3D 44.85 % 52.42 % 42.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
44 Fast VP-RCNN code 44.84 % 51.19 % 42.63 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
45 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
44.84 % 52.42 % 42.56 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
46 TBD 44.65 % 50.72 % 42.61 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
47 SVGA-Net
This method makes use of Velodyne laser scans.
44.57 % 51.45 % 42.45 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
48 anonymous code 44.50 % 50.60 % 42.26 % 0.05s 1 core @ >3.5 Ghz (python)
49 FSA-PVRCNN
This method makes use of Velodyne laser scans.
44.49 % 49.33 % 42.58 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
50 SIF 44.28 % 52.05 % 42.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
51 CVRS VIC-RCNN 44.13 % 48.95 % 42.42 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
52 MGACNet 44.12 % 50.98 % 41.62 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
53 CVIS-DF3D_v2 43.97 % 51.14 % 41.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
54 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 43.85 % 52.15 % 41.68 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
55 TBD 43.69 % 52.35 % 41.24 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
56 FPC3D_all
This method makes use of Velodyne laser scans.
43.41 % 50.05 % 41.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
57 tbd 43.41 % 49.45 % 40.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
58 MKFFNet 43.29 % 50.71 % 40.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 CVRS VIC-Net 43.11 % 49.25 % 41.35 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
60 3DBN_2 42.97 % 50.99 % 40.49 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
61 MSL3D 42.82 % 48.81 % 40.13 % 0.03 s GPU @ 2.5 Ghz (Python)
62 MKFFNet 42.58 % 49.79 % 40.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 XView 42.42 % 47.24 % 39.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 VGCN 42.33 % 50.02 % 40.05 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
65 IGRP+ 41.86 % 50.15 % 38.98 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
66 deprecated 41.85 % 47.88 % 40.09 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
67 AF_MCLS 41.61 % 50.55 % 37.85 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
68 MKFFNet 41.33 % 47.80 % 39.39 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
69 TBD 41.12 % 48.24 % 39.06 % 0.05 s GPU @ 2.5 Ghz (Python)
70 PFF3D
This method makes use of Velodyne laser scans.
40.94 % 48.74 % 38.54 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
71 DAMNET code 39.30 % 49.66 % 35.52 % 1 s 1 core @ 2.5 Ghz (C/C++)
72 NLK-3D 39.22 % 49.79 % 36.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
73 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.
74 BirdNet+
This method makes use of Velodyne laser scans.
code 38.28 % 45.53 % 35.37 % 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.
75 RoIFusion code 38.08 % 46.21 % 35.97 % 0.22 s 1 core @ 3.0 Ghz (Python)
76 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.
77 NLK-ALL code 37.61 % 47.88 % 33.86 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
78 IOU-SSD code 36.90 % 43.81 % 35.11 % 0.045s 1 core @ 2.5 Ghz (C/C++)
79 CBi-GNN-persons 36.56 % 45.80 % 32.68 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
80 Pointpillar_TV 35.28 % 42.65 % 33.10 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
81 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.
82 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.
83 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.
84 FCY
This method makes use of Velodyne laser scans.
32.64 % 41.16 % 29.35 % 0.02 s GPU @ 2.5 Ghz (Python)
85 CG-Stereo
This method uses stereo information.
29.56 % 39.24 % 25.87 % 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.
86 tiny-stereo-v2
This method uses stereo information.
29.50 % 40.58 % 26.01 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
87 Disp R-CNN
This method uses stereo information.
code 29.12 % 42.72 % 25.09 % 0.387 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.
88 stereo-tkc
This method uses stereo information.
28.44 % 37.74 % 25.86 % 0.4 s GPU @ 2.0 Ghz (Python + C/C++)
89 Disp R-CNN (velo)
This method uses stereo information.
code 28.34 % 40.21 % 24.46 % 0.387 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.
90 tiny-stereo
This method uses stereo information.
28.22 % 38.17 % 24.73 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
91 NCL code 27.06 % 31.78 % 25.63 % NA s 1 core @ 2.5 Ghz (Python)
92 OSE+ 26.02 % 36.60 % 22.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
93 VN3D 25.44 % 33.01 % 23.64 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
94 OSE
This method uses stereo information.
23.62 % 33.00 % 20.35 % 0.1 s GPU @ 2.5 Ghz (C/C++)
95 BirdNet
This method makes use of Velodyne laser scans.
23.06 % 28.20 % 21.65 % 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.
96 OC Stereo
This method uses stereo information.
code 20.80 % 29.79 % 18.62 % 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.
97 Stereo3D
This method uses stereo information.
20.76 % 31.01 % 18.41 % 0.1 s GPU 1080Ti
98 DSGN
This method uses stereo information.
code 20.75 % 26.61 % 18.86 % 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.
99 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.
100 AEC3D 18.05 % 23.60 % 15.35 % 0.01 s GPU @ 2.5 Ghz (Python)
101 SOD 15.49 % 23.56 % 13.38 % 0.1 s 1 core @ 2.5 Ghz (Python)
102 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.
103 RT3D-GMP
This method uses stereo information.
14.22 % 19.92 % 12.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
104 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.
105 CaDDN 9.41 % 14.72 % 8.17 % 0.63 s GPU @ 2.5 Ghz (Python)
106 RefinedMPL 7.92 % 13.09 % 7.25 % 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.
107 MonoRUn 7.59 % 11.70 % 6.34 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
108 MonoFlex 7.36 % 10.36 % 6.29 % 0.03 s GPU @ 2.5 Ghz (Python)
109 MonoPair 7.04 % 10.99 % 6.29 % 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.
110 DLE 6.96 % 10.73 % 6.20 % 0.04 s GPU @ 2.5 Ghz (Python)
111 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.
112 RelationNet3D_res18 6.29 % 9.28 % 5.29 % 0.04 s GPU @ 2.5 Ghz (Python)
113 DAMono3D 6.22 % 8.96 % 5.17 % 0.09s 1 core @ 2.5 Ghz (C/C++)
114 Deprecated 6.12 % 8.70 % 5.16 % Deprecated Deprecated
115 GA-Aug 5.86 % 8.83 % 4.77 % 0.04 s GPU @ 2.5 Ghz (Python)
116 MonoGeo 5.73 % 8.14 % 4.74 % 0.05 s 1 core @ 2.5 Ghz (Python)
117 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.
118 PG-MonoNet 5.43 % 7.06 % 4.55 % 0.19 s GPU @ 2.5 Ghz (Python)
119 PLDet3d 4.91 % 7.18 % 3.93 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
120 NL_M3D 4.66 % 6.20 % 3.99 % 0.2 s 1 core @ 2.5 Ghz (Python)
121 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.
122 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.
123 CDI3D 4.55 % 6.63 % 3.88 % 0.03 s GPU @ 2.5 Ghz (Python)
124 M3D-RPN(S-R) 4.46 % 6.53 % 4.10 % 0.16 s GPU @ 1.5 Ghz (Python)
125 FADNet code 4.45 % 6.46 % 3.70 % 0.04 s GPU @ >3.5 Ghz (Python)
126 modat3D
This is an online method (no batch processing).
4.23 % 6.62 % 3.39 % 0.03 s GPU @ 2.5 Ghz (Python)
127 MP-Mono 4.22 % 5.87 % 3.42 % 0.16 s GPU @ 2.5 Ghz (Python)
128 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 .
129 DDMP-3D 4.02 % 5.53 % 3.36 % 0.18 s 1 core @ 2.5 Ghz (Python)
130 DP3D 3.86 % 5.25 % 3.10 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
131 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. CVPR 2020.
132 Center3D 3.71 % 5.67 % 3.52 % 0.05 s GPU @ 3.5 Ghz (Python)
133 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.
134 LAPNet 3.59 % 4.86 % 2.98 % 0.03 s 1 core @ 2.5 Ghz (Python)
135 MonoEF code 3.05 % 4.61 % 2.85 % 0.03 s 1 core @ 2.5 Ghz (Python)
136 MTMono3d 2.38 % 3.11 % 1.89 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
137 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.
138 SparVox3D 2.05 % 2.90 % 1.69 % 0.05 s GPU @ 2.0 Ghz (Python)
139 UDI-mono3D 1.85 % 2.94 % 1.60 % 0.05 s 1 core @ 2.5 Ghz (Python)
140 UM3D_TUM 1.79 % 3.60 % 1.79 % 0.05 s 1 core @ 2.5 Ghz (Python)
141 UDI-mono3D 1.42 % 2.09 % 1.07 % 0.05 s 1 core @ 2.5 Ghz (Python)
142 PVNet 0.01 % 0.00 % 0.01 % 0,1 s 1 core @ 2.5 Ghz (Python)
143 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.
144 TBD 0.00 % 0.00 % 0.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
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.
75.24 % 89.91 % 67.01 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
2 HIKVISION-AFree 74.08 % 87.14 % 66.16 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
72.61 % 83.93 % 65.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
4 anonymous code 72.55 % 85.63 % 65.33 % 0.05s 1 core @ >3.5 Ghz (python)
5 SAA-PV-RCNN 72.24 % 84.12 % 64.70 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
6 Fast VP-RCNN code 72.07 % 84.39 % 65.02 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
7 GNN-RCNN 71.90 % 85.09 % 65.27 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
8 E^2-PV-RCNN 71.89 % 84.41 % 65.15 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
9 PV-RCNN-v2 71.86 % 84.60 % 63.84 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
10 HIKVISION-ADLab-HZ 71.75 % 85.66 % 65.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 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. CVPR 2020.
12 RangeIoUDet
This method makes use of Velodyne laser scans.
71.49 % 85.99 % 63.62 % 0.02 s 1 core @ 2.5 Ghz (Python)
13 HVNet 71.17 % 83.97 % 63.65 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
14 FPC-RCNN 70.93 % 83.75 % 63.47 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
15 CVRS VIC-RCNN 70.05 % 85.46 % 63.44 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
16 FSA-PVRCNN
This method makes use of Velodyne laser scans.
69.67 % 81.86 % 63.32 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
17 XView-PartA^2 69.43 % 83.48 % 63.18 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
18 TBD 69.41 % 82.71 % 61.77 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
19 CBi-GNN-persons 69.23 % 82.37 % 61.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
20 TBD 69.08 % 83.68 % 62.28 % 0.05 s GPU @ 2.5 Ghz (Python)
21 TBD 69.08 % 83.68 % 62.28 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
22 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 68.89 % 82.49 % 62.41 % 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.
23 F-ConvNet
This method makes use of Velodyne laser scans.
code 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.
24 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 68.73 % 83.43 % 61.85 % 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.
25 MSL3D 68.57 % 81.23 % 62.01 % 0.03 s GPU @ 2.5 Ghz (Python)
26 HotSpotNet 68.51 % 83.29 % 61.84 % 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.
27 NLK-ALL code 68.30 % 83.07 % 60.31 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
28 CVIS-DF3D_v2 68.21 % 80.74 % 60.44 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
29 CVRS VIC-Net 67.98 % 81.50 % 60.82 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
30 H^23D R-CNN 67.90 % 82.76 % 60.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
31 RoIFusion code 67.71 % 83.13 % 61.70 % 0.22 s 1 core @ 3.0 Ghz (Python)
32 3DSSD code 67.62 % 85.04 % 61.14 % 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.
33 MGACNet 67.40 % 82.29 % 60.71 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
34 TBD_IOU2 67.36 % 82.68 % 59.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
35 Point-GNN
This method makes use of Velodyne laser scans.
code 67.28 % 81.17 % 59.67 % 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.
36 PP-3D 67.28 % 81.17 % 59.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
37 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.
38 STD code 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.
39 KNN-GCNN 67.22 % 83.35 % 59.51 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
40 tbd 67.20 % 81.16 % 59.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
41 FPCR-CNN 67.17 % 82.51 % 60.33 % 0.05 s 1 core @ 2.5 Ghz (Python)
42 MKFFNet 67.10 % 80.98 % 60.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
43 TBD_IOU 67.09 % 82.97 % 59.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
44 VGCN 67.04 % 81.50 % 59.45 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
45 TBD_IOU1 66.95 % 81.77 % 58.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
46 SVGA-Net
This method makes use of Velodyne laser scans.
66.66 % 78.93 % 59.55 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
47 deprecated 66.47 % 78.62 % 60.14 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
48 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.
49 MKFFNet 66.16 % 81.36 % 58.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 MKFFNet 66.09 % 78.95 % 59.58 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
51 AF 66.00 % 83.03 % 57.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 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.
53 MVX-Net++ 64.84 % 78.89 % 58.15 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
54 FPC3D_all
This method makes use of Velodyne laser scans.
64.66 % 78.81 % 58.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
55 CCFNET 64.65 % 81.29 % 57.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
56 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 64.54 % 79.65 % 57.84 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
57 Baseline of CA RCNN 64.53 % 79.62 % 57.91 % 0.1 s GPU @ 2.5 Ghz (Python)
58 CVIS-DF3D 64.53 % 79.62 % 57.91 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
59 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
64.52 % 79.64 % 57.90 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
60 AF_MCLS 64.34 % 82.45 % 57.39 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
61 3DBN_2 64.28 % 81.06 % 57.55 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
62 SIF 64.13 % 79.32 % 57.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 GAP-soft-filter 64.02 % 79.39 % 57.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 TANet code 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.
65 SIEV-Net 63.21 % 82.22 % 56.41 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
66 XView 63.06 % 81.32 % 56.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 NLK-3D 62.97 % 80.61 % 56.52 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
68 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.
69 PF-GAP 62.49 % 78.64 % 55.87 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
70 FCY
This method makes use of Velodyne laser scans.
62.25 % 78.65 % 54.74 % 0.02 s GPU @ 2.5 Ghz (Python)
71 IOU-SSD code 61.78 % 74.11 % 55.70 % 0.045s 1 core @ 2.5 Ghz (C/C++)
72 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.
73 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.
74 Pointpillar_TV 59.26 % 74.78 % 52.33 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
75 Simple3D Net 59.03 % 75.72 % 52.42 % 0.02 s GPU @ 2.5 Ghz (Python)
76 IGRP+ 57.94 % 76.25 % 51.86 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
77 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.
78 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.
79 PFF3D
This method makes use of Velodyne laser scans.
55.71 % 72.67 % 49.58 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
80 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.
81 BirdNet+
This method makes use of Velodyne laser scans.
code 52.15 % 72.45 % 46.57 % 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.
82 DAMNET code 49.71 % 67.52 % 45.21 % 1 s 1 core @ 2.5 Ghz (C/C++)
83 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.
84 BirdNet
This method makes use of Velodyne laser scans.
41.56 % 58.64 % 36.94 % 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.
85 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.
86 stereo-tkc
This method uses stereo information.
38.55 % 56.90 % 33.70 % 0.4 s GPU @ 2.0 Ghz (Python + C/C++)
87 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.
88 tiny-stereo-v2
This method uses stereo information.
36.64 % 53.72 % 32.22 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
89 CG-Stereo
This method uses stereo information.
36.25 % 55.33 % 32.17 % 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.
90 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.
91 tiny-stereo
This method uses stereo information.
33.48 % 52.03 % 29.11 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
92 SOD 28.81 % 44.90 % 24.37 % 0.1 s 1 core @ 2.5 Ghz (Python)
93 Disp R-CNN (velo)
This method uses stereo information.
code 27.04 % 44.19 % 23.58 % 0.387 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.
94 Disp R-CNN
This method uses stereo information.
code 27.04 % 44.19 % 23.58 % 0.387 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.
95 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.
96 OSE+ 23.55 % 38.05 % 20.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
97 DSGN
This method uses stereo information.
code 21.04 % 31.23 % 18.93 % 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.
98 VN3D 20.21 % 28.35 % 19.31 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
99 OSE
This method uses stereo information.
19.41 % 32.06 % 17.42 % 0.1 s GPU @ 2.5 Ghz (C/C++)
100 OC Stereo
This method uses stereo information.
code 19.23 % 32.47 % 17.11 % 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.
101 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.
102 RT3D-GMP
This method uses stereo information.
13.92 % 20.59 % 12.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
103 AEC3D 13.24 % 20.80 % 12.09 % 0.01 s GPU @ 2.5 Ghz (Python)
104 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.
105 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.
106 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.
107 CaDDN 5.38 % 9.67 % 4.75 % 0.63 s GPU @ 2.5 Ghz (Python)
108 DAMono3D 4.18 % 7.05 % 4.31 % 0.09s 1 core @ 2.5 Ghz (C/C++)
109 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.
110 CDI3D 3.78 % 6.01 % 3.24 % 0.03 s GPU @ 2.5 Ghz (Python)
111 MonoGeo 3.33 % 5.42 % 2.87 % 0.05 s 1 core @ 2.5 Ghz (Python)
112 RelationNet3D_res18 3.32 % 6.59 % 3.13 % 0.04 s GPU @ 2.5 Ghz (Python)
113 DLE 3.28 % 5.34 % 2.83 % 0.04 s GPU @ 2.5 Ghz (Python)
114 DDMP-3D 3.14 % 4.92 % 2.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
115 modat3D
This is an online method (no batch processing).
3.02 % 5.71 % 2.73 % 0.03 s GPU @ 2.5 Ghz (Python)
116 MonoPair 2.87 % 4.76 % 2.42 % 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.
117 Deprecated 2.80 % 3.96 % 2.32 % Deprecated Deprecated
118 Center3D 2.76 % 5.28 % 2.72 % 0.05 s GPU @ 3.5 Ghz (Python)
119 MonoFlex 2.67 % 4.41 % 2.50 % 0.03 s GPU @ 2.5 Ghz (Python)
120 RefinedMPL 2.42 % 4.23 % 2.14 % 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.
121 UDI-mono3D 2.16 % 3.81 % 1.65 % 0.05 s 1 core @ 2.5 Ghz (Python)
122 UDI-mono3D 2.01 % 3.59 % 1.79 % 0.05 s 1 core @ 2.5 Ghz (Python)
123 NL_M3D 2.01 % 2.70 % 1.75 % 0.2 s 1 core @ 2.5 Ghz (Python)
124 PG-MonoNet 1.89 % 3.00 % 1.66 % 0.19 s GPU @ 2.5 Ghz (Python)
125 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.
126 DP3D 1.87 % 3.09 % 1.96 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
127 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. CVPR 2020.
128 GA-Aug 1.73 % 2.71 % 1.57 % 0.04 s GPU @ 2.5 Ghz (Python)
129 MP-Mono 1.58 % 2.43 % 1.70 % 0.16 s GPU @ 2.5 Ghz (Python)
130 PLDet3d 1.45 % 2.21 % 1.58 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
131 MTMono3d 1.30 % 2.06 % 1.06 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
132 MonoEF code 1.18 % 2.36 % 1.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
133 LAPNet 1.03 % 1.71 % 1.04 % 0.03 s 1 core @ 2.5 Ghz (Python)
134 FADNet code 0.94 % 1.54 % 0.79 % 0.04 s GPU @ >3.5 Ghz (Python)
135 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 .
136 MonoRUn 0.73 % 1.14 % 0.66 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
137 UM3D_TUM 0.62 % 0.45 % 0.62 % 0.05 s 1 core @ 2.5 Ghz (Python)
138 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.
139 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
140 TBD 0.00 % 0.00 % 0.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
141 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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