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 ADLAB 91.66 % 95.56 % 86.92 % 0.08 s 1 core @ >3.5 Ghz (C/C++)
2 SPANet 91.59 % 95.59 % 86.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
3 PVGNet 91.26 % 94.36 % 86.63 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
4 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.
5 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.
6 CN 90.50 % 94.51 % 85.86 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
7 Deformable PV-RCNN
This method makes use of Velodyne laser scans.
code 90.13 % 92.42 % 85.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya and K. Czarnecki: Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations. ECCV 2020 Perception for Autonomous Driving Workshop.
8 Associate-3Ddet_v2 90.00 % 95.55 % 84.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
9 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++)
10 AIMC-RUC 89.80 % 93.64 % 84.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
11 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.
12 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++)
13 deprecated 89.77 % 93.68 % 82.31 % deprecated deprecated
14 BorderAtt 89.76 % 94.67 % 86.73 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
15 CBi-GNN 89.74 % 95.92 % 84.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
16 OAP 89.72 % 93.13 % 82.25 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
17 D3D 89.72 % 93.37 % 84.72 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
18 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.
19 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.
20 Cas-SSD 89.47 % 93.31 % 84.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
21 FCY
This method makes use of Velodyne laser scans.
89.46 % 95.27 % 84.34 % 0.02 s GPU @ 2.5 Ghz (Python)
22 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++)
23 HUAWEI Octopus 89.39 % 92.58 % 86.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
24 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.
25 ISF 89.28 % 93.17 % 84.38 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
26 CJJ 89.20 % 92.90 % 84.30 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
27 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.
28 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.
29 PP-3D 89.17 % 93.11 % 83.90 % 0.1 s 1 core @ 2.5 Ghz (Python)
30 Noah CV Lab - SSL 89.16 % 90.18 % 81.73 % 0.1 s GPU @ 2.5 Ghz (Python)
31 RoIFusion code 89.06 % 92.90 % 83.96 % 0.22 s 1 core @ 3.0 Ghz (Python)
32 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.
33 CLOCs_PointCas 88.99 % 92.60 % 81.74 % 0.1 s GPU @ 2.5 Ghz (Python)
34 Discrete-PointDet 88.95 % 94.56 % 83.56 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
35 NLK-ALL code 88.89 % 92.25 % 84.13 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
36 Voxel R-CNN 88.83 % 94.85 % 86.13 % 0.04 s GPU @ 3.0 Ghz (C/C++)
37 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.
38 PointCSE 88.81 % 92.58 % 83.64 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
39 RangeRCNN-LV 88.81 % 92.41 % 85.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 F-3DNet 88.76 % 92.68 % 83.63 % 0.5 s GPU @ 2.5 Ghz (Python)
41 cvMax 88.64 % 92.12 % 83.72 % 0.04 s GPU @ >3.5 Ghz (Python)
42 deprecated 88.59 % 92.18 % 83.60 % 0.04 s GPU @ 2.5 Ghz (Python)
43 VICNet 88.58 % 92.27 % 83.36 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
44 ISF-v2 88.57 % 92.15 % 83.91 % 0.04 s 1 core @ 2.5 Ghz (Python)
45 KNN-GCNN 88.57 % 91.73 % 83.32 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
46 PVF-NET 88.57 % 92.20 % 83.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
47 IC-SECOND 88.57 % 91.94 % 85.43 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
48 MuRF 88.56 % 91.57 % 83.46 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
49 BLPNet_V2 88.55 % 92.24 % 83.44 % 0.04 s 1 core @ 2.5 Ghz (Python)
50 Chovy 88.54 % 92.34 % 83.68 % 0.04 s GPU @ 2.5 Ghz (Python)
51 nonet 88.49 % 91.97 % 85.33 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
52 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.
53 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.
54 deprecated 88.44 % 92.14 % 85.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
55 PC-RGNN 88.43 % 92.08 % 85.81 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
56 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.
57 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.
58 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.
59 AP-RCNN 88.35 % 92.16 % 85.64 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
60 ReFineNet 88.32 % 91.93 % 85.68 % 0.08 s 1 core @ 2.5 Ghz (Python)
61 OneCoLab SicNet V2 88.27 % 92.23 % 85.70 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
62 HyBrid Feature Det 88.27 % 92.09 % 85.69 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
63 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.
64 NLK-3D 88.22 % 91.54 % 83.33 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
65 CVRS_PF 88.22 % 91.81 % 84.91 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
66 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.
67 IC-PVRCNN 88.20 % 92.35 % 85.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
68 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.
69 SVGA-Net
This method makes use of Velodyne laser scans.
88.17 % 92.01 % 85.43 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
70 Baseline of CA RCNN 88.13 % 91.91 % 85.40 % 0.1 s GPU @ 2.5 Ghz (Python)
71 CVIS-DF3D 88.13 % 91.91 % 85.40 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
72 CA-RCNN 88.11 % 91.88 % 85.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 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.
74 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.
75 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.
76 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.
77 DEFT 88.06 % 92.06 % 83.22 % 1 s GPU @ 2.5 Ghz (Python)
78 CVIS-DF3D_v2 88.06 % 91.85 % 85.37 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
79 OneCoLab SicNet 88.06 % 92.17 % 83.60 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
80 deprecated 88.05 % 91.96 % 83.21 % 0.05 s GPU @ >3.5 Ghz (Python)
81 deprecated 88.04 % 91.97 % 83.22 % - -
82 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
88.03 % 91.88 % 85.18 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
83 Dccnet 88.01 % 92.09 % 82.45 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
84 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.
85 LZY_RCNN 87.94 % 91.74 % 83.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
86 tbd code 87.88 % 91.36 % 84.75 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
87 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.
88 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.
89 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++)
90 LZnet 87.77 % 91.46 % 82.92 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
91 MGACNet 87.68 % 90.93 % 84.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
92 deprecated 87.63 % 93.66 % 80.35 % 0.06 s GPU @ >3.5 Ghz (Python)
93 VAL 87.63 % 93.57 % 79.89 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
94 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.
95 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.
96 V3D 87.53 % 90.83 % 82.30 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
97 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.
98 AF_V1 87.47 % 92.70 % 82.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
99 deprecated 87.46 % 92.54 % 77.39 % 0.05 s 1 core @ 2.5 Ghz (Python)
100 IE-PointRCNN 87.43 % 92.11 % 81.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
101 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.
102 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.
103 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.
104 VAR 87.31 % 90.68 % 82.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
105 PiP 87.25 % 90.87 % 83.38 % 0.033 s 1 core @ 2.5 Ghz (Python)
106 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.
107 MDA 87.13 % 90.67 % 82.80 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
108 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.
109 Pointpillar_TV 87.08 % 90.50 % 81.98 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
110 EPENet 87.00 % 90.98 % 82.99 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
111 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.
112 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.
113 PointPiallars_SECA 86.79 % 90.15 % 82.87 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
114 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.
115 FLID 86.77 % 91.58 % 81.14 % 0.04 s GPU @ 2.5 Ghz (Python)
116 CentrNet-FG 86.72 % 90.30 % 82.99 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
117 CU-PointRCNN 86.69 % 92.65 % 82.66 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
118 tt code 86.68 % 90.57 % 81.98 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
119 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.
120 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.
121 MVX-Net++ 86.53 % 91.86 % 81.41 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
122 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.
123 RUC 86.46 % 90.06 % 82.20 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
124 Simple3D Net 86.46 % 89.82 % 82.60 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
125 PPFNet code 86.44 % 92.35 % 81.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
126 autonet 86.42 % 89.81 % 81.25 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
127 HR-SECOND code 86.40 % 91.68 % 81.40 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
128 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.
129 VOXEL_FPN_HR 86.36 % 90.28 % 81.20 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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130 IGRP+ 86.29 % 92.20 % 81.48 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
131 Bit 86.27 % 89.74 % 81.19 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
132 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.
133 IGRP 86.21 % 92.04 % 81.30 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
134 DPointNet 86.12 % 88.55 % 79.82 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
135 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.
136 RethinkDet3D 86.05 % 91.32 % 81.13 % 0.15 s 1 core @ 2.5 Ghz (Python)
137 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.
138 TBD 86.00 % 89.79 % 83.37 % 0.05 s GPU @ 2.5 Ghz (Python)
139 TBD 85.91 % 90.88 % 80.95 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
140 PPBA 85.85 % 91.30 % 80.92 % NA s GPU @ 2.5 Ghz (Python)
141 TBU 85.85 % 91.30 % 80.92 % NA s GPU @ 2.5 Ghz (Python)
142 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.
143 RUC code 85.84 % 88.54 % 81.15 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
144 BVVF 85.83 % 91.20 % 80.76 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
145 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.
146 PBASN code 85.62 % 90.95 % 80.49 % NA s GPU @ 2.5 Ghz (Python)
147 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.
148 3DBN_2 85.30 % 91.37 % 82.57 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
149 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++)
150 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.
151 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.
152 baseline 84.88 % 89.25 % 80.18 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
153 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.
154 Prune 84.81 % 90.48 % 77.40 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
155 autoRUC 84.80 % 90.44 % 77.43 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
156 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.
157 RUC code 84.40 % 89.11 % 79.33 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
158 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.
159 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.
160 DAMNET code 82.14 % 87.90 % 75.52 % 1 s 1 core @ 2.5 Ghz (C/C++)
161 voxelrcnn 81.41 % 88.21 % 75.26 % 15 s 1 core @ 2.5 Ghz (C/C++)
162 RuiRUC 80.20 % 86.90 % 67.77 % 0.12 s 1 core @ 2.5 Ghz (Python)
163 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.
164 NLK 79.15 % 82.59 % 72.65 % 0.02 s 1 core @ 2.5 Ghz (Python)
165 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.
166 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.
167 seivl 77.43 % 85.43 % 75.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
168 RCD 75.83 % 82.26 % 69.61 % 0.1 s GPU @ 2.5 Ghz (Python)
169 ANM 75.40 % 84.78 % 61.28 % 0.12 s 1 core @ 2.5 Ghz (Python)
170 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.
171 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.
172 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.
173 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.
174 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.
175 tiny-stereo-v1
This method uses stereo information.
66.55 % 85.74 % 57.55 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
176 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.
177 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++)
178 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.
179 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.
180 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.
181 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.
182 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.
183 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.
184 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.
185 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.
186 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.
187 Pseudo-LiDAR E2E
This method uses stereo information.
58.84 % 79.58 % 52.06 % 0.4 s GPU @ 2.5 Ghz (Python)
188 PB3D
This method uses stereo information.
58.04 % 79.75 % 49.78 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
189 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.
190 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.
191 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.
192 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.
193 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.
194 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.
195 RTS3D 51.79 % 72.17 % 43.19 % 0.03 s GPU @ 2.5 Ghz (Python)
196 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.
197 Stereo3D
This method uses stereo information.
50.28 % 76.10 % 36.86 % 0.1 s GPU 1080Ti
198 RT3D-GMP
This method uses stereo information.
49.57 % 61.28 % 38.70 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
199 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.
200 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.
201 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.
202 m-prcnn
This method uses stereo information.
42.81 % 67.82 % 33.63 % 0.43 s 1 core @ 2.5 Ghz (Python)
203 IDA-3D
This method uses stereo information.
42.47 % 61.87 % 34.59 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
204 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.
205 ASOD 33.63 % 54.61 % 26.76 % 0.28 s GPU @ 2.5 Ghz (Python)
206 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.
207 deprecated 30.56 % 34.56 % 25.69 % 1 core @ 2.5 Ghz (C/C++)
208 S3D 30.44 % 35.25 % 25.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
209 LNET 29.68 % 34.30 % 25.11 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
210 Det3D 20.80 % 35.46 % 16.00 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
211 ITS-MDPL 19.52 % 32.80 % 16.96 % 0.16 s GPU @ 2.5 Ghz (Python)
212 PSMD 19.33 % 28.63 % 15.31 % 0.1 s GPU @ 2.5 Ghz (Python)
213 I2BEV 18.91 % 27.94 % 17.19 % 0.63 s GPU @ 2.5 Ghz (Python)
214 MTMono3d 18.54 % 27.00 % 15.71 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
215 IAFA 17.88 % 25.88 % 15.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
216 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.
217 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 .
218 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.
219 YoloMono3D 17.15 % 26.79 % 12.56 % 0.05 s GPU @ 2.5 Ghz (Python)
220 OCM3D 17.13 % 27.87 % 13.53 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
221 IMA 17.08 % 23.93 % 14.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
222 MCA 17.07 % 25.93 % 14.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
223 vxnet 17.00 % 22.58 % 13.10 % 1 s 1 core @ 2.5 Ghz (C/C++)
224 DP3D 16.96 % 26.51 % 12.82 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
225 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.
226 UM3D_TUM 16.69 % 23.63 % 14.17 % 0.05 s 1 core @ 2.5 Ghz (Python)
227 PG-MonoNet 16.31 % 23.31 % 13.03 % 0.19 s GPU @ 2.5 Ghz (Python)
228 SSL-RTM3D 16.20 % 23.44 % 14.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
229 CDI3D 16.06 % 22.06 % 13.43 % 0.03 s GPU @ 2.5 Ghz (Python)
230 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.
231 MP-Mono 16.01 % 23.45 % 12.07 % 0.16 s GPU @ 2.5 Ghz (Python)
232 NL_M3D 15.93 % 24.15 % 12.11 % 0.2 s 1 core @ 2.5 Ghz (Python)
233 DA-3Ddet 15.90 % 23.35 % 12.11 % 0.4 s GPU @ 2.5 Ghz (Python)
234 LAPNet 15.76 % 25.10 % 12.30 % 0.03 s 1 core @ 2.5 Ghz (Python)
235 DP3D 15.44 % 23.98 % 12.24 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
236 MA 15.43 % 22.01 % 14.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
237 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.
238 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.
239 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.
240 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.
241 Mono3CN 14.17 % 19.82 % 12.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
242 LCD3D 13.99 % 21.97 % 11.43 % 0.03 s GPU @ 2.5 Ghz (Python)
243 Center3D 13.98 % 18.89 % 12.44 % 0.05 s GPU @ 3.5 Ghz (Python)
244 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.
245 SS3D_HW 13.70 % 20.28 % 9.86 % 0.4 s GPU @ 2.5 Ghz (Python)
246 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 .
247 SSL-RTM3D Res18 13.37 % 19.71 % 11.10 % 0.02 s GPU @ 2.5 Ghz (Python)
248 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.
249 RAR-Net 13.01 % 20.63 % 10.19 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
250 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.
251 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.
252 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.
253 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.
254 anonymous 10.96 % 20.42 % 9.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
255 OACV 10.13 % 16.24 % 8.28 % 0.23 s GPU @ 2.5 Ghz (Python)
256 anonymous 10.06 % 18.80 % 8.56 % 1 s 1 core @ 2.5 Ghz (C/C++)
257 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.
258 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.
259 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.
260 AACL 6.75 % 8.55 % 5.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
261 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.
262 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.
263 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.
264 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.
265 SparVox3D 4.16 % 6.41 % 3.74 % 0.05 s GPU @ 2.0 Ghz (Python)
266 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.
267 UDI-mono3D 3.08 % 3.93 % 2.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
268 UDI-mono3D 2.79 % 3.38 % 2.37 % 0.05 s 1 core @ 2.5 Ghz (Python)
269 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.
270 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 .
271 ANM 0.00 % 0.00 % 0.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
272 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
273 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.
274 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 Noah CV Lab - SSL 50.66 % 57.27 % 46.55 % 0.1 s GPU @ 2.5 Ghz (Python)
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 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.
14 PPBA 49.34 % 57.23 % 46.86 % NA s GPU @ 2.5 Ghz (Python)
15 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.
16 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.
17 RethinkDet3D 48.84 % 58.96 % 46.20 % 0.15 s 1 core @ 2.5 Ghz (Python)
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 PiP 48.14 % 56.16 % 45.27 % 0.033 s 1 core @ 2.5 Ghz (Python)
21 MVX-Net++ 48.04 % 56.63 % 45.44 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
22 PPFNet code 47.92 % 55.04 % 44.95 % 0.1 s 1 core @ 2.5 Ghz (Python + 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 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.
30 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.
31 Deformable PV-RCNN
This method makes use of Velodyne laser scans.
code 45.82 % 52.03 % 43.81 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya and K. Czarnecki: Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations. ECCV 2020 Perception for Autonomous Driving Workshop.
32 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.
33 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.
34 AP-RCNN 45.02 % 53.73 % 41.88 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
35 CA-RCNN 44.98 % 52.44 % 42.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
36 Baseline of CA RCNN 44.85 % 52.42 % 42.56 % 0.1 s GPU @ 2.5 Ghz (Python)
37 CVIS-DF3D 44.85 % 52.42 % 42.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
38 SVGA-Net
This method makes use of Velodyne laser scans.
44.84 % 52.42 % 42.56 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
39 IC-PVRCNN 44.13 % 48.95 % 42.42 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
40 MGACNet 44.12 % 50.98 % 41.62 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
41 CVIS-DF3D_v2 43.97 % 51.14 % 41.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
42 IC-SECOND 43.11 % 49.25 % 41.35 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
43 3DBN_2 42.97 % 50.99 % 40.49 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
44 IGRP+ 41.86 % 50.15 % 38.98 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
45 deprecated 41.85 % 47.88 % 40.09 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
46 VOXEL_FPN_HR 41.62 % 50.18 % 38.30 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
47 TBD 41.12 % 48.24 % 39.06 % 0.05 s GPU @ 2.5 Ghz (Python)
48 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++)
49 PBASN code 40.63 % 46.80 % 38.41 % NA s GPU @ 2.5 Ghz (Python)
50 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
40.30 % 47.68 % 38.42 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
51 HR-SECOND code 40.06 % 50.05 % 36.47 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
52 DAMNET code 39.30 % 49.66 % 35.52 % 1 s 1 core @ 2.5 Ghz (C/C++)
53 NLK-3D 39.22 % 49.79 % 36.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
54 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.
55 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.
56 LZnet 38.25 % 44.40 % 36.32 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
57 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.
58 NLK-ALL code 37.61 % 47.88 % 33.86 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
59 Pointpillar_TV 35.28 % 42.65 % 33.10 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
60 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.
61 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.
62 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.
63 FCY
This method makes use of Velodyne laser scans.
32.64 % 41.16 % 29.35 % 0.02 s GPU @ 2.5 Ghz (Python)
64 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.
65 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.
66 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.
67 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.
68 PB3D
This method uses stereo information.
23.62 % 33.00 % 20.35 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
69 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.
70 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.
71 Stereo3D
This method uses stereo information.
20.76 % 31.01 % 18.41 % 0.1 s GPU 1080Ti
72 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.
73 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.
74 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.
75 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.
76 I2BEV 9.41 % 14.72 % 8.17 % 0.63 s GPU @ 2.5 Ghz (Python)
77 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.
78 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.
79 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.
80 vxnet 6.26 % 8.93 % 5.29 % 1 s 1 core @ 2.5 Ghz (C/C++)
81 RT3D-GMP
This method uses stereo information.
5.73 % 7.93 % 5.62 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
82 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.
83 SS3D_HW 5.47 % 8.81 % 4.79 % 0.4 s GPU @ 2.5 Ghz (Python)
84 PG-MonoNet 5.43 % 7.06 % 4.55 % 0.19 s GPU @ 2.5 Ghz (Python)
85 NL_M3D 4.66 % 6.20 % 3.99 % 0.2 s 1 core @ 2.5 Ghz (Python)
86 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.
87 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.
88 CDI3D 4.55 % 6.63 % 3.88 % 0.03 s GPU @ 2.5 Ghz (Python)
89 MP-Mono 4.22 % 5.87 % 3.42 % 0.16 s GPU @ 2.5 Ghz (Python)
90 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 .
91 Mono3CN 4.02 % 6.03 % 3.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 DP3D 4.01 % 5.71 % 3.64 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
93 DP3D 3.86 % 5.25 % 3.10 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
94 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.
95 Center3D 3.71 % 5.67 % 3.52 % 0.05 s GPU @ 3.5 Ghz (Python)
96 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.
97 LAPNet 3.59 % 4.86 % 2.98 % 0.03 s 1 core @ 2.5 Ghz (Python)
98 MTMono3d 2.38 % 3.11 % 1.89 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
99 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.
100 UDI-mono3D 1.85 % 2.94 % 1.60 % 0.05 s 1 core @ 2.5 Ghz (Python)
101 UM3D_TUM 1.79 % 3.60 % 1.79 % 0.05 s 1 core @ 2.5 Ghz (Python)
102 UDI-mono3D 1.42 % 2.09 % 1.07 % 0.05 s 1 core @ 2.5 Ghz (Python)
103 SparVox3D 0.44 % 0.55 % 0.30 % 0.05 s GPU @ 2.0 Ghz (Python)
104 PVNet 0.01 % 0.00 % 0.01 % 0,1 s 1 core @ 2.5 Ghz (Python)
105 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 Deformable PV-RCNN
This method makes use of Velodyne laser scans.
code 72.61 % 83.93 % 65.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya and K. Czarnecki: Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations. ECCV 2020 Perception for Autonomous Driving Workshop.
4 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.
5 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.
6 IC-PVRCNN 70.05 % 85.46 % 63.44 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
7 TBD 69.08 % 83.68 % 62.28 % 0.05 s GPU @ 2.5 Ghz (Python)
8 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.
9 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.
10 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.
11 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.
12 NLK-ALL code 68.30 % 83.07 % 60.31 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
13 CVIS-DF3D_v2 68.21 % 80.74 % 60.44 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
14 IC-SECOND 67.98 % 81.50 % 60.82 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
15 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.
16 MGACNet 67.40 % 82.29 % 60.71 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
17 PPBA 67.28 % 82.69 % 60.53 % NA s GPU @ 2.5 Ghz (Python)
18 TBU 67.28 % 82.69 % 60.53 % NA s GPU @ 2.5 Ghz (Python)
19 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.
20 PP-3D 67.28 % 81.17 % 59.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
21 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.
22 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.
23 KNN-GCNN 67.22 % 83.35 % 59.51 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
24 deprecated 66.47 % 78.62 % 60.14 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
25 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
66.44 % 83.57 % 59.79 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
26 RethinkDet3D 66.42 % 82.73 % 59.60 % 0.15 s 1 core @ 2.5 Ghz (Python)
27 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.
28 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.
29 PiP 65.12 % 79.51 % 58.25 % 0.033 s 1 core @ 2.5 Ghz (Python)
30 VOXEL_FPN_HR 65.02 % 81.07 % 58.44 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
31 MVX-Net++ 64.84 % 78.89 % 58.15 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
32 Baseline of CA RCNN 64.53 % 79.62 % 57.91 % 0.1 s GPU @ 2.5 Ghz (Python)
33 CVIS-DF3D 64.53 % 79.62 % 57.91 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
34 SVGA-Net
This method makes use of Velodyne laser scans.
64.52 % 79.64 % 57.90 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
35 3DBN_2 64.28 % 81.06 % 57.55 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
36 HR-SECOND code 64.21 % 78.79 % 57.82 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
37 CA-RCNN 64.02 % 79.39 % 57.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 LZnet 63.79 % 79.28 % 57.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
39 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.
40 PBASN code 63.34 % 79.45 % 57.01 % NA s GPU @ 2.5 Ghz (Python)
41 VICNet 63.21 % 82.22 % 56.41 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
42 NLK-3D 62.97 % 80.61 % 56.52 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
43 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.
44 AP-RCNN 62.49 % 78.64 % 55.87 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
45 FCY
This method makes use of Velodyne laser scans.
62.25 % 78.65 % 54.74 % 0.02 s GPU @ 2.5 Ghz (Python)
46 CentrNet-FG 62.10 % 76.94 % 54.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
47 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.
48 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.
49 Pointpillar_TV 59.26 % 74.78 % 52.33 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
50 Simple3D Net 59.03 % 75.72 % 52.42 % 0.02 s GPU @ 2.5 Ghz (Python)
51 IGRP+ 57.94 % 76.25 % 51.86 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
52 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.
53 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.
54 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++)
55 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.
56 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.
57 DAMNET code 49.71 % 67.52 % 45.21 % 1 s 1 core @ 2.5 Ghz (C/C++)
58 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.
59 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.
60 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.
61 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.
62 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.
63 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.
64 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.
65 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.
66 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.
67 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.
68 PB3D
This method uses stereo information.
19.41 % 32.06 % 17.42 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
69 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.
70 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.
71 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.
72 RT3D-GMP
This method uses stereo information.
6.90 % 10.09 % 6.14 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
73 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.
74 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.
75 I2BEV 5.38 % 9.67 % 4.75 % 0.63 s GPU @ 2.5 Ghz (Python)
76 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.
77 CDI3D 3.78 % 6.01 % 3.24 % 0.03 s GPU @ 2.5 Ghz (Python)
78 vxnet 2.88 % 4.94 % 2.50 % 1 s 1 core @ 2.5 Ghz (C/C++)
79 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.
80 SS3D_HW 2.78 % 5.03 % 2.36 % 0.4 s GPU @ 2.5 Ghz (Python)
81 Center3D 2.76 % 5.28 % 2.72 % 0.05 s GPU @ 3.5 Ghz (Python)
82 Mono3CN 2.69 % 3.92 % 2.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 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.
84 UDI-mono3D 2.16 % 3.81 % 1.65 % 0.05 s 1 core @ 2.5 Ghz (Python)
85 UDI-mono3D 2.01 % 3.59 % 1.79 % 0.05 s 1 core @ 2.5 Ghz (Python)
86 NL_M3D 2.01 % 2.70 % 1.75 % 0.2 s 1 core @ 2.5 Ghz (Python)
87 PG-MonoNet 1.89 % 3.00 % 1.66 % 0.19 s GPU @ 2.5 Ghz (Python)
88 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.
89 DP3D 1.87 % 3.09 % 1.96 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
90 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.
91 MP-Mono 1.58 % 2.43 % 1.70 % 0.16 s GPU @ 2.5 Ghz (Python)
92 DP3D 1.57 % 2.32 % 1.29 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
93 MTMono3d 1.30 % 2.06 % 1.06 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
94 LAPNet 1.03 % 1.71 % 1.04 % 0.03 s 1 core @ 2.5 Ghz (Python)
95 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 .
96 UM3D_TUM 0.62 % 0.45 % 0.62 % 0.05 s 1 core @ 2.5 Ghz (Python)
97 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.
98 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
99 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

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Citation

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}



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