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 Fast VP-RCNN code 90.46 % 92.83 % 85.94 % 0.05 s 1 core @ >3.5 Ghz (python)
9 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++)
10 Associate-3Ddet_v2 90.00 % 95.55 % 84.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
11 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++)
12 EBM3DOD 89.86 % 95.64 % 84.56 % 0.08 s 1 core @ 2.5 Ghz (Python)
13 CIA-SSD
This method makes use of Velodyne laser scans.
89.84 % 93.74 % 82.39 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
14 HRI-ADLab-HZ 89.83 % 93.21 % 84.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
15 AIMC-RUC 89.80 % 93.64 % 84.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
16 CLOCs_PVCas 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.
17 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++)
18 deprecated 89.77 % 93.68 % 82.31 % deprecated deprecated
19 CM3DV 89.77 % 95.54 % 84.49 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
20 EA-M-RCNN(BorderAtt) 89.76 % 94.67 % 86.73 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
21 CBi-GNN 89.74 % 95.92 % 84.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
22 OAP 89.72 % 93.13 % 82.25 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
23 D3D 89.72 % 93.37 % 84.72 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
24 EBM3DOD baseline 89.63 % 95.44 % 84.34 % 0.08 s 1 core @ 2.5 Ghz (Python)
25 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.
26 scssd-normal(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: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.
27 Cas-SSD 89.47 % 93.31 % 84.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 FCY
This method makes use of Velodyne laser scans.
89.46 % 95.27 % 84.34 % 0.02 s GPU @ 2.5 Ghz (Python)
29 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++)
30 LZnet 89.39 % 93.36 % 81.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
31 HUAWEI Octopus 89.39 % 92.58 % 86.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
32 scssd-normal(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: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.
33 ISF 89.28 % 93.17 % 84.38 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
34 CJJ 89.20 % 92.90 % 84.30 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
35 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.
36 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.
37 PP-3D 89.17 % 93.11 % 83.90 % 0.1 s 1 core @ 2.5 Ghz (Python)
38 Noah CV Lab - SSL 89.16 % 90.18 % 81.73 % 0.1 s GPU @ 2.5 Ghz (Python)
39 RoIFusion code 89.06 % 92.90 % 83.96 % 0.22 s 1 core @ 3.0 Ghz (Python)
40 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.
41 Discrete-PointDet 88.95 % 94.56 % 83.56 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
42 NLK-ALL code 88.89 % 92.25 % 84.13 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
43 Voxel R-CNN 88.83 % 94.85 % 86.13 % 0.04 s GPU @ 3.0 Ghz (C/C++)
44 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.
45 PointCSE 88.81 % 92.58 % 83.64 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
46 RangeRCNN-LV 88.81 % 92.41 % 85.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 F-3DNet 88.76 % 92.68 % 83.63 % 0.5 s GPU @ 2.5 Ghz (Python)
48 PV-RCNN-v2 88.74 % 92.66 % 85.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
49 cvMax 88.64 % 92.12 % 83.72 % 0.04 s GPU @ >3.5 Ghz (Python)
50 deprecated 88.59 % 92.18 % 83.60 % 0.04 s GPU @ 2.5 Ghz (Python)
51 RangeIoUDet
This method makes use of Velodyne laser scans.
88.59 % 92.28 % 85.83 % 0.02 s 1 core @ 2.5 Ghz (Python)
52 VICNet 88.58 % 92.27 % 83.36 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
53 ISF-v2 88.57 % 92.15 % 83.91 % 0.04 s 1 core @ 2.5 Ghz (Python)
54 KNN-GCNN 88.57 % 91.73 % 83.32 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
55 PVF-NET 88.57 % 92.20 % 83.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
56 CVRS VIC-Net 88.57 % 91.94 % 85.43 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
57 MuRF 88.56 % 91.57 % 83.46 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
58 BLPNet_V2 88.55 % 92.24 % 83.44 % 0.04 s 1 core @ 2.5 Ghz (Python)
59 Chovy 88.54 % 92.34 % 83.68 % 0.04 s GPU @ 2.5 Ghz (Python)
60 nonet 88.49 % 91.97 % 85.33 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
61 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.
62 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.
63 deprecated 88.44 % 92.14 % 85.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
64 SimpleDET 88.44 % 92.00 % 85.90 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
65 PC-RGNN 88.43 % 92.08 % 85.81 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
66 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.
67 MSG-PGNN 88.40 % 91.97 % 85.87 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
68 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.
69 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.
70 PF-GAP 88.35 % 92.16 % 85.64 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
71 ReFineNet 88.32 % 91.93 % 85.68 % 0.08 s 1 core @ 2.5 Ghz (Python)
72 HyBrid Feature Det 88.27 % 92.09 % 85.69 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
73 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.
74 MSL3D 88.23 % 91.64 % 85.53 % 0.03 s GPU @ 2.5 Ghz (Python)
75 Multi-Sensor3D 88.23 % 91.64 % 85.53 % 0.03 s GPU @ 2.5 Ghz (Python)
76 NLK-3D 88.22 % 91.54 % 83.33 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
77 CVRS_PF 88.22 % 91.81 % 84.91 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
78 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++)
79 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.
80 CVRS VIC-RCNN 88.20 % 92.35 % 85.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
81 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.
82 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++)
83 Baseline of CA RCNN 88.13 % 91.91 % 85.40 % 0.1 s GPU @ 2.5 Ghz (Python)
84 CVIS-DF3D 88.13 % 91.91 % 85.40 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
85 GAP-soft-filter 88.11 % 91.88 % 85.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
86 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.
87 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.
88 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.
89 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.
90 CVIS-DF3D_v2 88.06 % 91.85 % 85.37 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
91 Dccnet 88.01 % 92.09 % 82.45 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
92 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.
93 CCFNET 87.97 % 94.25 % 83.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
94 HVPR 87.94 % 91.76 % 83.03 % 0.02 s GPU @ 2.5 Ghz (Python)
95 LZY_RCNN 87.94 % 91.74 % 83.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
96 tbd code 87.88 % 91.36 % 84.75 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
97 XView-PartA^2 87.84 % 91.94 % 85.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
98 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.
99 TBD 87.83 % 91.80 % 85.19 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
100 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.
101 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++)
102 MGACNet 87.68 % 90.93 % 84.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
103 deprecated 87.63 % 93.66 % 80.35 % 0.06 s GPU @ >3.5 Ghz (Python)
104 VAL 87.63 % 93.57 % 79.89 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
105 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.
106 VOXEL_3D 87.55 % 90.83 % 82.24 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
107 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.
108 V3D 87.53 % 90.83 % 82.30 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
109 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.
110 AF_V1 87.47 % 92.70 % 82.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
111 PKRCNN 87.41 % 91.62 % 84.67 % 0.01s 1 core @ 2.5 Ghz (Python)
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112 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.
113 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.
114 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.
115 VAR 87.31 % 90.68 % 82.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
116 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.
117 MDA 87.13 % 90.67 % 82.80 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
118 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.
119 Pointpillar_TV 87.08 % 90.50 % 81.98 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
120 EPENet 87.00 % 90.98 % 82.99 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
121 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.
122 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.
123 PointPiallars_SECA 86.79 % 90.15 % 82.87 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
124 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.
125 FLID 86.77 % 91.58 % 81.14 % 0.04 s GPU @ 2.5 Ghz (Python)
126 CentrNet-FG 86.72 % 90.30 % 82.99 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
127 CU-PointRCNN 86.69 % 92.65 % 82.66 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
128 tt code 86.68 % 90.57 % 81.98 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
129 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.
130 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.
131 MVX-Net++ 86.53 % 91.86 % 81.41 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
132 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.
133 Simple3D Net 86.46 % 89.82 % 82.60 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
134 autonet 86.42 % 89.81 % 81.25 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
135 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.
136 IGRP+ 86.29 % 92.20 % 81.48 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
137 Bit 86.27 % 89.74 % 81.19 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
138 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.
139 IGRP 86.21 % 92.04 % 81.30 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
140 DPointNet 86.12 % 88.55 % 79.82 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
141 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.
142 RethinkDet3D 86.05 % 91.32 % 81.13 % 0.15 s 1 core @ 2.5 Ghz (Python)
143 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.
144 TBD 86.00 % 89.79 % 83.37 % 0.05 s GPU @ 2.5 Ghz (Python)
145 TBD 85.91 % 90.88 % 80.95 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
146 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.
147 PPBA 85.85 % 91.30 % 80.92 % NA s GPU @ 2.5 Ghz (Python)
148 TBU 85.85 % 91.30 % 80.92 % NA s GPU @ 2.5 Ghz (Python)
149 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.
150 RUC code 85.84 % 88.54 % 81.15 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
151 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.
152 APL-Second 85.70 % 90.78 % 78.69 % 0.05 s 1 core @ 2.5 Ghz (Python)
153 PBASN code 85.62 % 90.95 % 80.49 % NA s GPU @ 2.5 Ghz (Python)
154 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.
155 3DBN_2 85.30 % 91.37 % 82.57 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
156 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++)
157 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.
158 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.
159 baseline 84.88 % 89.25 % 80.18 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
160 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.
161 Prune 84.81 % 90.48 % 77.40 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
162 autoRUC 84.80 % 90.44 % 77.43 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
163 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.
164 RUC code 84.40 % 89.11 % 79.33 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
165 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.
166 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.
167 DAMNET code 82.14 % 87.90 % 75.52 % 1 s 1 core @ 2.5 Ghz (C/C++)
168 voxelrcnn 81.41 % 88.21 % 75.26 % 15 s 1 core @ 2.5 Ghz (C/C++)
169 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.
170 NLK 79.15 % 82.59 % 72.65 % 0.02 s 1 core @ 2.5 Ghz (Python)
171 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.
172 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.
173 seivl 77.43 % 85.43 % 75.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
174 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.
175 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.
176 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.
177 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.
178 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.
179 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.
180 tiny-stereo-v1
This method uses stereo information.
66.68 % 85.24 % 57.62 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
181 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.
182 tiny-stereo-v2
This method uses stereo information.
66.41 % 85.91 % 57.27 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
183 PLUME
This method uses stereo information.
66.27 % 82.97 % 56.70 % 0.15 s GPU @ 2.5 Ghz (Python)
184 CDN
This method uses stereo information.
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.
185 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
65.74 % 74.20 % 58.35 % 0.5 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
186 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.
187 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.
188 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.
189 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.
190 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. arXiv preprint arXiv:2007.03085 2020.
191 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.
192 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.
193 OSE
This method uses stereo information.
58.04 % 79.75 % 49.78 % 0.1 s GPU @ 2.5 Ghz (C/C++)
194 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.
195 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.
196 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.
197 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.
198 Disp R-CNN
This method uses stereo information.
code 52.37 % 73.87 % 43.67 % 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.
199 Disp R-CNN (velo)
This method uses stereo information.
code 52.37 % 74.12 % 43.79 % 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.
200 RTS3D 51.79 % 72.17 % 43.19 % 0.03 s GPU @ 2.5 Ghz (Python)
201 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.
202 Stereo3D
This method uses stereo information.
50.28 % 76.10 % 36.86 % 0.1 s GPU 1080Ti
203 RT3D-GMP
This method uses stereo information.
49.57 % 61.28 % 38.70 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
204 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.
205 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.
206 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.
207 IDA-3D
This method uses stereo information.
42.47 % 61.87 % 34.59 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
208 SC(DLA34+DCO)
This method uses stereo information.
42.12 % 62.97 % 35.37 % 0.07 s GPU @ 2.5 Ghz (Python)
209 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.
210 ASOD 33.63 % 54.61 % 26.76 % 0.28 s GPU @ 2.5 Ghz (Python)
211 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.
212 deprecated 30.56 % 34.56 % 25.69 % 1 core @ 2.5 Ghz (C/C++)
213 S3D 30.44 % 35.25 % 25.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
214 LNET 29.68 % 34.30 % 25.11 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
215 Det3D 20.80 % 35.46 % 16.00 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
216 MonoFlex 19.75 % 28.23 % 16.89 % 0.03 s GPU @ 2.5 Ghz (Python)
217 MonoEF code 19.70 % 29.03 % 17.26 % 0.03 s 1 core @ 2.5 Ghz (Python)
218 ITS-MDPL 19.52 % 32.80 % 16.96 % 0.16 s GPU @ 2.5 Ghz (Python)
219 PSMD 19.33 % 28.63 % 15.31 % 0.1 s GPU @ 2.5 Ghz (Python)
220 CaDDN 18.91 % 27.94 % 17.19 % 0.63 s GPU @ 2.5 Ghz (Python)
221 DLE 18.89 % 24.79 % 16.00 % 0.04 s GPU @ 2.5 Ghz (Python)
222 Object Transformer 18.78 % 26.43 % 15.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
223 MTMono3d 18.54 % 27.00 % 15.71 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
224 DDMP-3D 17.89 % 28.08 % 13.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
225 IAFA 17.88 % 25.88 % 15.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
226 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.
227 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 .
228 MonoRUn 17.34 % 27.94 % 15.24 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
229 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.
230 Deprecated 17.22 % 23.59 % 13.34 % Deprecated Deprecated
231 DAMono3D 17.17 % 23.73 % 13.46 % 0.09s 1 core @ 2.5 Ghz (C/C++)
232 YoloMono3D 17.15 % 26.79 % 12.56 % 0.05 s GPU @ 2.5 Ghz (Python)
233 OCM3D 17.13 % 27.87 % 13.53 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
234 IMA 17.08 % 23.93 % 14.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
235 MCA 17.07 % 25.93 % 14.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
236 DP3D 16.96 % 26.51 % 12.82 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
237 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.
238 UM3D_TUM 16.69 % 23.63 % 14.17 % 0.05 s 1 core @ 2.5 Ghz (Python)
239 GA-Aug 16.45 % 24.64 % 14.15 % 0.04 s GPU @ 2.5 Ghz (Python)
240 PG-MonoNet 16.31 % 23.31 % 13.03 % 0.19 s GPU @ 2.5 Ghz (Python)
241 SSL-RTM3D 16.20 % 23.44 % 14.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
242 CDI3D 16.06 % 22.06 % 13.43 % 0.03 s GPU @ 2.5 Ghz (Python)
243 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.
244 MP-Mono 16.01 % 23.45 % 12.07 % 0.16 s GPU @ 2.5 Ghz (Python)
245 NL_M3D 15.93 % 24.15 % 12.11 % 0.2 s 1 core @ 2.5 Ghz (Python)
246 DA-3Ddet 15.90 % 23.35 % 12.11 % 0.4 s GPU @ 2.5 Ghz (Python)
247 LAPNet 15.76 % 25.10 % 12.30 % 0.03 s 1 core @ 2.5 Ghz (Python)
248 DP3D 15.44 % 23.98 % 12.24 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
249 MA 15.43 % 22.01 % 14.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
250 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.
251 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.
252 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.
253 FADNet code 14.22 % 23.00 % 12.56 % 0.04 s GPU @ >3.5 Ghz (Python)
254 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.
255 Mono3CN 14.17 % 19.82 % 12.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
256 LCD3D 13.99 % 21.97 % 11.43 % 0.03 s GPU @ 2.5 Ghz (Python)
257 Center3D 13.98 % 18.89 % 12.44 % 0.05 s GPU @ 3.5 Ghz (Python)
258 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.
259 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 .
260 SSL-RTM3D Res18 13.37 % 19.71 % 11.10 % 0.02 s GPU @ 2.5 Ghz (Python)
261 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.
262 RAR-Net 13.01 % 20.63 % 10.19 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
263 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.
264 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.
265 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.
266 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.
267 anonymous 10.96 % 20.42 % 9.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
268 anonymous 10.06 % 18.80 % 8.56 % 1 s 1 core @ 2.5 Ghz (C/C++)
269 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.
270 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.
271 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.
272 AACL 6.75 % 8.55 % 5.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
273 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.
274 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.
275 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.
276 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.
277 SparVox3D 4.16 % 6.41 % 3.74 % 0.05 s GPU @ 2.0 Ghz (Python)
278 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.
279 UDI-mono3D 3.08 % 3.93 % 2.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
280 UDI-mono3D 2.79 % 3.38 % 2.37 % 0.05 s 1 core @ 2.5 Ghz (Python)
281 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.
282 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 .
283 GAA 0.00 % 0.00 % 0.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
284 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
285 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.
286 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 VICNet 52.15 % 60.78 % 48.54 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
2 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.
3 CentrNet-FG 50.87 % 60.56 % 48.16 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
4 HRI-ADLab-HZ-AFree 50.67 % 56.54 % 48.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 Noah CV Lab - SSL 50.66 % 57.27 % 46.55 % 0.1 s GPU @ 2.5 Ghz (Python)
6 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.
7 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.
8 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.
9 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.
10 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.
11 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.
12 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.
13 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.
14 HRI-ADLab-HZ 49.62 % 55.94 % 47.39 % 0.1 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 PPBA 49.34 % 57.23 % 46.86 % NA s GPU @ 2.5 Ghz (Python)
17 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.
18 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.
19 RethinkDet3D 48.84 % 58.96 % 46.20 % 0.15 s 1 core @ 2.5 Ghz (Python)
20 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.
21 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.
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 TBU 46.76 % 55.15 % 44.60 % NA s GPU @ 2.5 Ghz (Python)
28 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.
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 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.
32 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++)
33 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.
34 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.
35 Fast VP-RCNN code 45.07 % 51.16 % 42.92 % 0.05 s 1 core @ >3.5 Ghz (python)
36 PF-GAP 45.02 % 53.73 % 41.88 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
37 GAP-soft-filter 44.98 % 52.44 % 42.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 Baseline of CA RCNN 44.85 % 52.42 % 42.56 % 0.1 s GPU @ 2.5 Ghz (Python)
39 CVIS-DF3D 44.85 % 52.42 % 42.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
40 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++)
41 TBD 44.65 % 50.72 % 42.61 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
42 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++)
43 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++)
44 CVRS VIC-RCNN 44.13 % 48.95 % 42.42 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
45 MGACNet 44.12 % 50.98 % 41.62 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
46 CVIS-DF3D_v2 43.97 % 51.14 % 41.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
47 CVRS VIC-Net 43.11 % 49.25 % 41.35 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
48 3DBN_2 42.97 % 50.99 % 40.49 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
49 MSL3D 42.82 % 48.81 % 40.13 % 0.03 s GPU @ 2.5 Ghz (Python)
50 IGRP+ 41.86 % 50.15 % 38.98 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
51 deprecated 41.85 % 47.88 % 40.09 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
52 AF_MCLS 41.61 % 50.55 % 37.85 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
53 PKRCNN 41.33 % 47.80 % 39.39 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
54 TBD 41.12 % 48.24 % 39.06 % 0.05 s GPU @ 2.5 Ghz (Python)
55 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++)
56 PBASN code 40.63 % 46.80 % 38.41 % NA s GPU @ 2.5 Ghz (Python)
57 DAMNET code 39.30 % 49.66 % 35.52 % 1 s 1 core @ 2.5 Ghz (C/C++)
58 NLK-3D 39.22 % 49.79 % 36.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
59 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.
60 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.
61 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.
62 NLK-ALL code 37.61 % 47.88 % 33.86 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
63 CBi-GNN-persons 36.56 % 45.80 % 32.68 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
64 Pointpillar_TV 35.28 % 42.65 % 33.10 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
65 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.
66 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.
67 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.
68 FCY
This method makes use of Velodyne laser scans.
32.64 % 41.16 % 29.35 % 0.02 s GPU @ 2.5 Ghz (Python)
69 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
29.77 % 37.16 % 26.61 % 0.5 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
70 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.
71 Disp R-CNN
This method uses stereo information.
code 25.36 % 36.06 % 21.62 % 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.
72 Disp R-CNN (velo)
This method uses stereo information.
code 24.95 % 35.39 % 21.30 % 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.
73 OSE
This method uses stereo information.
23.62 % 33.00 % 20.35 % 0.1 s GPU @ 2.5 Ghz (C/C++)
74 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.
75 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.
76 Stereo3D
This method uses stereo information.
20.76 % 31.01 % 18.41 % 0.1 s GPU 1080Ti
77 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.
78 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.
79 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.
80 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.
81 CaDDN 9.41 % 14.72 % 8.17 % 0.63 s GPU @ 2.5 Ghz (Python)
82 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.
83 MonoRUn 7.59 % 11.70 % 6.34 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
84 MonoFlex 7.36 % 10.36 % 6.29 % 0.03 s GPU @ 2.5 Ghz (Python)
85 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.
86 DLE 6.96 % 10.73 % 6.20 % 0.04 s GPU @ 2.5 Ghz (Python)
87 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.
88 DAMono3D 6.22 % 8.96 % 5.17 % 0.09s 1 core @ 2.5 Ghz (C/C++)
89 Deprecated 6.12 % 8.70 % 5.16 % Deprecated Deprecated
90 GA-Aug 5.86 % 8.83 % 4.77 % 0.04 s GPU @ 2.5 Ghz (Python)
91 RT3D-GMP
This method uses stereo information.
5.73 % 7.93 % 5.62 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
92 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.
93 PG-MonoNet 5.43 % 7.06 % 4.55 % 0.19 s GPU @ 2.5 Ghz (Python)
94 NL_M3D 4.66 % 6.20 % 3.99 % 0.2 s 1 core @ 2.5 Ghz (Python)
95 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.
96 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.
97 CDI3D 4.55 % 6.63 % 3.88 % 0.03 s GPU @ 2.5 Ghz (Python)
98 M3D-RPN(S-R) 4.46 % 6.53 % 4.10 % 0.16 s GPU @ 1.5 Ghz (Python)
99 FADNet code 4.45 % 6.46 % 3.70 % 0.04 s GPU @ >3.5 Ghz (Python)
100 MP-Mono 4.22 % 5.87 % 3.42 % 0.16 s GPU @ 2.5 Ghz (Python)
101 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 .
102 Mono3CN 4.02 % 6.03 % 3.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
103 DDMP-3D 4.02 % 5.53 % 3.36 % 0.18 s 1 core @ 2.5 Ghz (Python)
104 DP3D 4.01 % 5.71 % 3.64 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
105 DP3D 3.86 % 5.25 % 3.10 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
106 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.
107 Center3D 3.71 % 5.67 % 3.52 % 0.05 s GPU @ 3.5 Ghz (Python)
108 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.
109 LAPNet 3.59 % 4.86 % 2.98 % 0.03 s 1 core @ 2.5 Ghz (Python)
110 MonoEF code 3.05 % 4.61 % 2.85 % 0.03 s 1 core @ 2.5 Ghz (Python)
111 MTMono3d 2.38 % 3.11 % 1.89 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
112 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.
113 UDI-mono3D 1.85 % 2.94 % 1.60 % 0.05 s 1 core @ 2.5 Ghz (Python)
114 UM3D_TUM 1.79 % 3.60 % 1.79 % 0.05 s 1 core @ 2.5 Ghz (Python)
115 UDI-mono3D 1.42 % 2.09 % 1.07 % 0.05 s 1 core @ 2.5 Ghz (Python)
116 SparVox3D 0.44 % 0.55 % 0.30 % 0.05 s GPU @ 2.0 Ghz (Python)
117 PVNet 0.01 % 0.00 % 0.01 % 0,1 s 1 core @ 2.5 Ghz (Python)
118 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.
75.24 % 89.91 % 67.01 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
2 Noah CV Lab - SSL 74.45 % 85.96 % 64.23 % 0.1 s GPU @ 2.5 Ghz (Python)
3 HRI-ADLab-HZ-AFree 74.08 % 87.14 % 66.16 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 Fast VP-RCNN code 73.69 % 87.08 % 66.03 % 0.05 s 1 core @ >3.5 Ghz (python)
5 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++)
6 PV-RCNN-v2 71.86 % 84.60 % 63.84 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
7 HRI-ADLab-HZ 71.75 % 85.66 % 65.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 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.
9 RangeIoUDet
This method makes use of Velodyne laser scans.
71.49 % 85.99 % 63.62 % 0.02 s 1 core @ 2.5 Ghz (Python)
10 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.
11 CVRS VIC-RCNN 70.05 % 85.46 % 63.44 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
12 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++)
13 XView-PartA^2 69.43 % 83.48 % 63.18 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
14 TBD 69.41 % 82.71 % 61.77 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
15 CBi-GNN-persons 69.23 % 82.37 % 61.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
16 TBD 69.08 % 83.68 % 62.28 % 0.05 s GPU @ 2.5 Ghz (Python)
17 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.
18 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.
19 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.
20 MSL3D 68.57 % 81.23 % 62.01 % 0.03 s GPU @ 2.5 Ghz (Python)
21 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.
22 NLK-ALL code 68.30 % 83.07 % 60.31 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
23 CVIS-DF3D_v2 68.21 % 80.74 % 60.44 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
24 CVRS VIC-Net 67.98 % 81.50 % 60.82 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
25 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.
26 MGACNet 67.40 % 82.29 % 60.71 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
27 PPBA 67.28 % 82.69 % 60.53 % NA s GPU @ 2.5 Ghz (Python)
28 TBU 67.28 % 82.69 % 60.53 % NA s GPU @ 2.5 Ghz (Python)
29 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.
30 PP-3D 67.28 % 81.17 % 59.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
31 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.
32 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.
33 KNN-GCNN 67.22 % 83.35 % 59.51 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
34 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++)
35 deprecated 66.47 % 78.62 % 60.14 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
36 RethinkDet3D 66.42 % 82.73 % 59.60 % 0.15 s 1 core @ 2.5 Ghz (Python)
37 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.
38 PKRCNN 66.09 % 78.95 % 59.58 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
39 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.
40 MVX-Net++ 64.84 % 78.89 % 58.15 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
41 CCFNET 64.65 % 81.29 % 57.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
42 Baseline of CA RCNN 64.53 % 79.62 % 57.91 % 0.1 s GPU @ 2.5 Ghz (Python)
43 CVIS-DF3D 64.53 % 79.62 % 57.91 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
44 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++)
45 AF_MCLS 64.34 % 82.45 % 57.39 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
46 3DBN_2 64.28 % 81.06 % 57.55 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
47 GAP-soft-filter 64.02 % 79.39 % 57.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 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.
49 PBASN code 63.34 % 79.45 % 57.01 % NA s GPU @ 2.5 Ghz (Python)
50 VICNet 63.21 % 82.22 % 56.41 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
51 NLK-3D 62.97 % 80.61 % 56.52 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
52 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.
53 PF-GAP 62.49 % 78.64 % 55.87 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
54 FCY
This method makes use of Velodyne laser scans.
62.25 % 78.65 % 54.74 % 0.02 s GPU @ 2.5 Ghz (Python)
55 CentrNet-FG 62.10 % 76.94 % 54.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
56 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.
57 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.
58 Pointpillar_TV 59.26 % 74.78 % 52.33 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
59 Simple3D Net 59.03 % 75.72 % 52.42 % 0.02 s GPU @ 2.5 Ghz (Python)
60 IGRP+ 57.94 % 76.25 % 51.86 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
61 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.
62 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.
63 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++)
64 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.
65 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.
66 DAMNET code 49.71 % 67.52 % 45.21 % 1 s 1 core @ 2.5 Ghz (C/C++)
67 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.
68 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.
69 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.
70 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.
71 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.
72 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.
73 Disp R-CNN (velo)
This method uses stereo information.
code 26.46 % 43.41 % 22.46 % 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.
74 Disp R-CNN
This method uses stereo information.
code 26.46 % 43.41 % 22.46 % 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.
75 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.
76 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.
77 OSE
This method uses stereo information.
19.41 % 32.06 % 17.42 % 0.1 s GPU @ 2.5 Ghz (C/C++)
78 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.
79 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.
80 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.
81 RT3D-GMP
This method uses stereo information.
6.90 % 10.09 % 6.14 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
82 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.
83 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.
84 CaDDN 5.38 % 9.67 % 4.75 % 0.63 s GPU @ 2.5 Ghz (Python)
85 DAMono3D 4.18 % 7.05 % 4.31 % 0.09s 1 core @ 2.5 Ghz (C/C++)
86 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.
87 CDI3D 3.78 % 6.01 % 3.24 % 0.03 s GPU @ 2.5 Ghz (Python)
88 DLE 3.28 % 5.34 % 2.83 % 0.04 s GPU @ 2.5 Ghz (Python)
89 DDMP-3D 3.14 % 4.92 % 2.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
90 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.
91 Deprecated 2.80 % 3.96 % 2.32 % Deprecated Deprecated
92 Center3D 2.76 % 5.28 % 2.72 % 0.05 s GPU @ 3.5 Ghz (Python)
93 Mono3CN 2.69 % 3.92 % 2.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
94 MonoFlex 2.67 % 4.41 % 2.50 % 0.03 s GPU @ 2.5 Ghz (Python)
95 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.
96 UDI-mono3D 2.16 % 3.81 % 1.65 % 0.05 s 1 core @ 2.5 Ghz (Python)
97 UDI-mono3D 2.01 % 3.59 % 1.79 % 0.05 s 1 core @ 2.5 Ghz (Python)
98 NL_M3D 2.01 % 2.70 % 1.75 % 0.2 s 1 core @ 2.5 Ghz (Python)
99 PG-MonoNet 1.89 % 3.00 % 1.66 % 0.19 s GPU @ 2.5 Ghz (Python)
100 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.
101 DP3D 1.87 % 3.09 % 1.96 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
102 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.
103 GA-Aug 1.73 % 2.71 % 1.57 % 0.04 s GPU @ 2.5 Ghz (Python)
104 MP-Mono 1.58 % 2.43 % 1.70 % 0.16 s GPU @ 2.5 Ghz (Python)
105 DP3D 1.57 % 2.32 % 1.29 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
106 MTMono3d 1.30 % 2.06 % 1.06 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
107 MonoEF code 1.18 % 2.36 % 1.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
108 LAPNet 1.03 % 1.71 % 1.04 % 0.03 s 1 core @ 2.5 Ghz (Python)
109 FADNet code 0.94 % 1.54 % 0.79 % 0.04 s GPU @ >3.5 Ghz (Python)
110 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 .
111 MonoRUn 0.73 % 1.14 % 0.66 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
112 UM3D_TUM 0.62 % 0.45 % 0.62 % 0.05 s 1 core @ 2.5 Ghz (Python)
113 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.
114 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
115 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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