3D Object Detection Evaluation 2017


The 3D object detection benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

We evaluate 3D object detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require an 3D bounding box overlap of 70%, while for pedestrians and cyclists we require a 3D bounding box overlap of 50%. Difficulties are defined as follows:

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 HIKVISION-ADLab-HZ 82.83 % 89.00 % 76.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 SE-SSD
This method makes use of Velodyne laser scans.
82.54 % 91.49 % 77.15 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
3 EA-M-RCNN(BorderAtt) 82.33 % 87.77 % 77.37 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
4 HUAWEI Octopus 82.13 % 88.26 % 77.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 ADLAB 82.08 % 90.92 % 77.36 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
6 PV-RCNN-v2 81.88 % 90.14 % 77.15 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
7 RangeRCNN-LV 81.85 % 88.76 % 77.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 PVGNet 81.81 % 89.94 % 77.09 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
9 E^2-PV-RCNN 81.70 % 88.33 % 77.20 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
10 PLNL-3DSSD
This method makes use of Velodyne laser scans.
81.69 % 88.98 % 74.90 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
11 DomainAdp+PVRCNN
This method makes use of Velodyne laser scans.
81.66 % 88.64 % 77.08 % 0.09 s GPU @ 2.5 Ghz (Python)
12 Fast VP-RCNN code 81.62 % 90.97 % 76.90 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
13 Voxel R-CNN code 81.62 % 90.90 % 77.06 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . arXiv preprint arXiv:2012.15712 2020.
14 HyBrid Feature Det 81.59 % 88.77 % 76.92 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
15 3DIoU+++ 81.58 % 88.53 % 77.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
16 CityBrainLab-TSD 81.57 % 88.13 % 77.00 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
17 CVRS VIC-RCNN 81.57 % 88.60 % 77.09 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
18 H^23D R-CNN 81.55 % 90.43 % 77.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
19 anonymous code 81.55 % 90.94 % 76.74 % 0.05s 1 core @ >3.5 Ghz (python)
20 LZY_RCNN 81.52 % 88.77 % 78.59 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
21 TBD 81.51 % 88.96 % 77.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 SIENet 81.50 % 87.83 % 77.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
23 MSG-PGNN 81.50 % 88.70 % 76.88 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
24 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
81.46 % 88.25 % 76.96 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
25 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 81.43 % 90.25 % 76.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
26 PC-RGNN 81.38 % 87.94 % 76.88 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
27 XView 81.35 % 89.21 % 76.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 RangeRCNN
This method makes use of Velodyne laser scans.
81.33 % 88.47 % 77.09 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
29 FSA-PVRCNN
This method makes use of Velodyne laser scans.
81.31 % 88.01 % 76.75 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
30 ReFineNet 81.24 % 87.70 % 76.77 % 0.08 s 1 core @ 2.5 Ghz (Python)
31 MSL3D 81.15 % 87.27 % 76.56 % 0.03 s GPU @ 2.5 Ghz (Python)
32 Multi-Sensor3D 81.15 % 87.27 % 76.56 % 0.03 s GPU @ 2.5 Ghz (Python)
33 SAA-PV-RCNN 81.09 % 87.24 % 78.05 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
34 FPC-RCNN 81.08 % 88.68 % 76.46 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
35 AIMC-RUC 80.83 % 90.14 % 73.59 % 0.11 s 1 core @ 2.5 Ghz (Python)
36 SVGA-Net
This method makes use of Velodyne laser scans.
80.82 % 87.40 % 76.23 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
37 GNN-RCNN 80.81 % 87.94 % 76.53 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
38 Associate-3Ddet_v2 80.77 % 91.53 % 75.23 % 0.04 s 1 core @ 2.5 Ghz (Python)
39 CIA-SSD v2
This method makes use of Velodyne laser scans.
80.71 % 89.61 % 75.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
40 CLOCs_PVCas code 80.67 % 88.94 % 77.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
41 AIMC-RUC 80.63 % 89.90 % 75.32 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
42 OAP 80.63 % 89.18 % 73.04 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
43 HRI-MSP-L
This method makes use of Velodyne laser scans.
80.62 % 87.61 % 76.29 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
44 CVRS VIC-Net 80.61 % 88.25 % 75.83 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
45 CVIS-DF3D_v2 80.48 % 87.20 % 76.01 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
46 XView-PartA^2 80.41 % 87.72 % 76.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
47 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
80.38 % 87.73 % 76.27 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
48 SPANet 80.34 % 91.05 % 74.89 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
49 CVRS_PF 80.33 % 88.04 % 75.21 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
50 AM-SSD 80.30 % 89.58 % 75.02 % 0.04 s 1 core @ 2.5 Ghz (Python)
51 CIA-SSD
This method makes use of Velodyne laser scans.
code 80.28 % 89.59 % 72.87 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
52 Baseline of CA RCNN 80.28 % 87.45 % 76.21 % 0.1 s GPU @ 2.5 Ghz (Python)
53 CVIS-DF3D 80.28 % 87.45 % 76.21 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
54 TBD 80.24 % 87.67 % 76.27 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
55 GAP-soft-filter 80.18 % 87.43 % 76.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
56 CBi-GNN 80.18 % 91.50 % 74.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
57 TBD 80.17 % 86.83 % 75.96 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
58 deprecated 80.16 % 89.48 % 72.75 % deprecated deprecated
59 EBM3DOD code 80.12 % 91.05 % 72.78 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
60 3D-CVF at SPA
This method makes use of Velodyne laser scans.
80.05 % 89.20 % 73.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
61 CN 79.89 % 90.55 % 76.31 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
62 SIF 79.88 % 86.84 % 75.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 VAL 79.87 % 89.35 % 70.27 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
64 CM3DV 79.87 % 89.00 % 72.59 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
65 RangeIoUDet
This method makes use of Velodyne laser scans.
79.80 % 88.60 % 76.76 % 0.02 s 1 core @ 2.5 Ghz (Python)
66 SA-SSD code 79.79 % 88.75 % 74.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
67 Seg-RCNN code 79.73 % 89.16 % 72.28 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
68 CJJ 79.72 % 88.98 % 74.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
69 STD code 79.71 % 87.95 % 75.09 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
70 PSS 79.71 % 89.13 % 74.78 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
71 AF_V1 79.68 % 88.16 % 72.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
72 FCY
This method makes use of Velodyne laser scans.
79.67 % 89.19 % 74.35 % 0.02 s GPU @ 2.5 Ghz (Python)
73 CDE-Net(0.3) 79.59 % 88.97 % 72.51 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Multi sensor fusion 3d object detection method. Submitted to OSA 2021.
74 3DSSD code 79.57 % 88.36 % 74.55 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
75 PointRes
This method makes use of Velodyne laser scans.
79.55 % 88.73 % 74.17 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
76 EBM3DOD baseline code 79.52 % 88.80 % 72.30 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
77 Cas-SSD 79.50 % 88.73 % 72.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
78 CDE-Net(0.4) 79.49 % 88.70 % 74.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Multi sensor fusion 3d object detection method. Submitted to OSA 2021.
79 Point-GNN
This method makes use of Velodyne laser scans.
code 79.47 % 88.33 % 72.29 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
80 PP-3D 79.47 % 88.33 % 72.29 % 0.1 s 1 core @ 2.5 Ghz (Python)
81 nonet 79.42 % 88.28 % 75.77 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
82 RoIFusion code 79.36 % 88.09 % 72.51 % 0.22 s 1 core @ 3.0 Ghz (Python)
83 3DIoU_v2 79.30 % 88.22 % 76.96 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
84 EPNet code 79.28 % 89.81 % 74.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
85 PF-GAP 79.27 % 87.65 % 76.43 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
86 FPCR-CNN 79.25 % 88.45 % 75.69 % 0.05 s 1 core @ 2.5 Ghz (Python)
87 3DIoU++ 79.22 % 87.49 % 76.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 MGACNet 79.18 % 86.20 % 74.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
89 D3D 79.15 % 87.07 % 73.79 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
90 NLK-ALL code 79.13 % 87.23 % 74.30 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
91 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 79.05 % 87.45 % 76.14 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
92 3D IoU-Net 79.03 % 87.96 % 72.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
93 CCFNET 78.97 % 88.20 % 74.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
94 SERCNN
This method makes use of Velodyne laser scans.
78.96 % 87.74 % 74.30 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
95 deprecated 78.83 % 87.89 % 73.52 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
96 FPC3D
This method makes use of the epipolar geometry.
78.81 % 87.61 % 75.49 % 33 s 1 core @ 2.5 Ghz (C/C++)
97 FLID 78.78 % 86.73 % 71.24 % 0.04 s GPU @ 2.5 Ghz (Python)
98 MVAF-Net code 78.71 % 87.87 % 75.48 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
99 ISF-v2 78.67 % 87.54 % 74.03 % 0.04 s 1 core @ 2.5 Ghz (Python)
100 PVF-NET 78.58 % 87.05 % 71.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
101 BLPNet_V2 78.57 % 87.10 % 71.67 % 0.04 s 1 core @ 2.5 Ghz (Python)
102 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.49 % 87.81 % 73.51 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
103 F-3DNet 78.48 % 85.48 % 71.62 % 0.5 s GPU @ 2.5 Ghz (Python)
104 CLOCs_SecCas 78.45 % 86.38 % 72.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
105 Patches - EMP
This method makes use of Velodyne laser scans.
78.41 % 89.84 % 73.15 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
106 MKFFNet 78.40 % 85.25 % 73.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
107 deprecated 78.32 % 89.34 % 71.21 % 0.06 s GPU @ >3.5 Ghz (Python)
108 HotSpotNet 78.31 % 87.60 % 73.34 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
109 MKFFNet 78.30 % 87.25 % 73.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
110 MKFFNet 78.30 % 86.86 % 73.80 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
111 KNN-GCNN 78.26 % 86.37 % 71.14 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
112 HV 77.92 % 86.38 % 73.04 % 0.02 s GPU @ 2.5 Ghz (Python)
113 CenterNet3D 77.90 % 86.20 % 73.03 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
114 V3D 77.87 % 86.58 % 72.52 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
115 tbd code 77.72 % 86.09 % 72.53 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
116 VOXEL_3D 77.69 % 86.45 % 72.20 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
117 VGCN 77.65 % 84.47 % 73.36 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
118 UberATG-MMF
This method makes use of Velodyne laser scans.
77.43 % 88.40 % 70.22 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
119 Associate-3Ddet code 77.40 % 85.99 % 70.53 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
120 Fast Point R-CNN
This method makes use of Velodyne laser scans.
77.40 % 85.29 % 70.24 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
121 Dccnet 77.22 % 86.67 % 69.97 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
122 Patches
This method makes use of Velodyne laser scans.
77.20 % 88.67 % 71.82 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
123 VAR 77.08 % 84.92 % 72.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
124 CU-PointRCNN 76.87 % 86.55 % 73.17 % 0.1 s GPU @ 1.5 Ghz (Python + C/C++)
125 HRI-VoxelFPN 76.70 % 85.64 % 69.44 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
126 SARPNET 76.64 % 85.63 % 71.31 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
127 TBD 76.57 % 85.33 % 72.05 % 0.05 s GPU @ 2.5 Ghz (Python)
128 IGRP+ 76.54 % 86.90 % 71.77 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
129 3D IoU Loss
This method makes use of Velodyne laser scans.
76.50 % 86.16 % 71.39 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
130 F-ConvNet
This method makes use of Velodyne laser scans.
code 76.39 % 87.36 % 66.69 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
131 DPointNet 76.34 % 81.67 % 70.34 % 0.07s 1 core @ 2.5 Ghz (C/C++)
132 SIEV-Net 76.18 % 85.21 % 70.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
133 SegVoxelNet 76.13 % 86.04 % 70.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
134 NLK-3D 76.08 % 84.47 % 70.93 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
135 TANet code 75.94 % 84.39 % 68.82 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
136 IGRP 75.90 % 86.27 % 69.31 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
137 MVX-Net++ 75.86 % 85.99 % 70.70 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
138 PointRGCN 75.73 % 85.97 % 70.60 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
139 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 75.64 % 86.96 % 70.70 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
140 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 75.43 % 86.10 % 68.88 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
141 MDA 75.39 % 83.72 % 71.98 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
142 R-GCN 75.26 % 83.42 % 68.73 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
143 epBRM
This method makes use of Velodyne laser scans.
code 75.15 % 85.00 % 69.84 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
144 3DBN_2 75.06 % 84.90 % 72.10 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
145 MAFF-Net(DAF-Pillar) 75.04 % 85.52 % 67.61 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
146 PI-RCNN 74.82 % 84.37 % 70.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
147 FPGNN 74.77 % 83.82 % 67.93 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
148 FPC3D_all
This method makes use of Velodyne laser scans.
74.55 % 85.50 % 69.91 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
149 Pointpillar_TV 74.55 % 83.08 % 69.13 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
150 PointPillars
This method makes use of Velodyne laser scans.
code 74.31 % 82.58 % 68.99 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
151 IOU-SSD code 74.09 % 85.00 % 68.42 % 0.045s 1 core @ 2.5 Ghz (C/C++)
152 Simple3D Net 74.06 % 83.06 % 69.17 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
153 ARPNET 74.04 % 84.69 % 68.64 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
154 tt code 73.92 % 84.14 % 69.15 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
155 PC-CNN-V2
This method makes use of Velodyne laser scans.
73.79 % 85.57 % 65.65 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
156 C-GCN 73.62 % 83.49 % 67.01 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
157 baseline 73.55 % 82.92 % 67.42 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
158 LSNet 73.55 % 86.13 % 68.58 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
159 3DBN
This method makes use of Velodyne laser scans.
73.53 % 83.77 % 66.23 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
160 SCNet
This method makes use of Velodyne laser scans.
73.17 % 83.34 % 67.93 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
161 TBD 73.02 % 82.74 % 67.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
162 PFF3D
This method makes use of Velodyne laser scans.
72.93 % 81.11 % 67.24 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
163 APL-Second 72.87 % 82.75 % 67.91 % 0.05 s 1 core @ 2.5 Ghz (Python)
164 DASS 72.31 % 81.85 % 65.99 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. 2020.
165 AVOD-FPN
This method makes use of Velodyne laser scans.
code 71.76 % 83.07 % 65.73 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
166 PointPainting
This method makes use of Velodyne laser scans.
71.70 % 82.11 % 67.08 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
167 WS3D
This method makes use of Velodyne laser scans.
70.59 % 80.99 % 64.23 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
168 F-PointNet
This method makes use of Velodyne laser scans.
code 69.79 % 82.19 % 60.59 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
169 UberATG-ContFuse
This method makes use of Velodyne laser scans.
68.78 % 83.68 % 61.67 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
170 MLOD
This method makes use of Velodyne laser scans.
code 67.76 % 77.24 % 62.05 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
171 AVOD
This method makes use of Velodyne laser scans.
code 66.47 % 76.39 % 60.23 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
172 DAMNET code 65.52 % 76.25 % 59.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
173 voxelrcnn 64.77 % 73.60 % 60.05 % 15 s 1 core @ 2.5 Ghz (C/C++)
174 MV3D
This method makes use of Velodyne laser scans.
63.63 % 74.97 % 54.00 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
175 KMC code 62.74 % 74.45 % 56.76 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
176 RCD 60.56 % 70.54 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
177 stereo-tkc
This method uses stereo information.
59.21 % 78.26 % 52.47 % 0.4 s GPU @ 2.0 Ghz (Python + C/C++)
178 tiny-stereo-v2
This method uses stereo information.
57.11 % 76.87 % 50.05 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
179 A3DODWTDA
This method makes use of Velodyne laser scans.
code 56.82 % 62.84 % 48.12 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
180 tiny-stereo
This method uses stereo information.
56.44 % 79.17 % 48.07 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
181 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 54.88 % 68.38 % 49.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
182 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
54.54 % 68.35 % 49.16 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
183 CDN
This method uses stereo information.
code 54.22 % 74.52 % 46.36 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
184 CG-Stereo
This method uses stereo information.
53.58 % 74.39 % 46.50 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
185 AEC3D 52.50 % 66.83 % 48.48 % 0.01 s GPU @ 2.5 Ghz (Python)
186 DSGN
This method uses stereo information.
code 52.18 % 73.50 % 45.14 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
187 VN3D 52.16 % 64.68 % 48.17 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
188 BirdNet+
This method makes use of Velodyne laser scans.
code 51.85 % 70.14 % 50.03 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
189 NCL code 50.07 % 46.58 % 50.33 % NA s 1 core @ 2.5 Ghz (Python)
190 SOD 48.69 % 70.90 % 40.12 % 0.1 s 1 core @ 2.5 Ghz (Python)
191 Complexer-YOLO
This method makes use of Velodyne laser scans.
47.34 % 55.93 % 42.60 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
192 Disp R-CNN (velo)
This method uses stereo information.
code 45.78 % 68.21 % 37.73 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
193 CDN-PL++
This method uses stereo information.
44.86 % 64.31 % 38.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
194 Disp R-CNN
This method uses stereo information.
code 43.27 % 67.02 % 36.43 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
195 OSE
This method uses stereo information.
43.27 % 64.78 % 37.13 % 0.1 s GPU @ 2.5 Ghz (C/C++)
196 Pseudo-LiDAR++
This method uses stereo information.
code 42.43 % 61.11 % 36.99 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
197 OSE+ 41.60 % 62.67 % 35.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
198 Stereo3D
This method uses stereo information.
41.25 % 65.68 % 30.42 % 0.1 s GPU 1080Ti
199 RT3D-GMP
This method uses stereo information.
38.76 % 45.79 % 30.00 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
200 ZoomNet
This method uses stereo information.
code 38.64 % 55.98 % 30.97 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
201 OC Stereo
This method uses stereo information.
code 37.60 % 55.15 % 30.25 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
202 RTS3D 37.38 % 58.51 % 31.12 % 0.03 s GPU @ 2.5 Ghz (Python)
203 Pseudo-Lidar
This method uses stereo information.
code 34.05 % 54.53 % 28.25 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
204 SC(DLA34+DCO)
This method uses stereo information.
31.30 % 49.94 % 25.62 % 0.07 s GPU @ 2.5 Ghz (Python)
205 Stereo R-CNN
This method uses stereo information.
code 30.23 % 47.58 % 23.72 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
206 BirdNet
This method makes use of Velodyne laser scans.
27.26 % 40.99 % 25.32 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
207 NVNet(BEV-3D) 24.87 % 33.30 % 21.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
208 RT3DStereo
This method uses stereo information.
23.28 % 29.90 % 18.96 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
209 RT3D
This method makes use of Velodyne laser scans.
19.14 % 23.74 % 18.86 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
210 StereoFENet
This method uses stereo information.
18.41 % 29.14 % 14.20 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
211 LGDet3d 14.82 % 22.73 % 12.88 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
212 ITS-MDPL 14.23 % 24.26 % 11.95 % 0.16 s GPU @ 2.5 Ghz (Python)
213 MonoFlex 13.89 % 19.94 % 12.07 % 0.03 s GPU @ 2.5 Ghz (Python)
214 MonoEF code 13.87 % 21.29 % 11.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
215 CaDDN 13.41 % 19.17 % 11.46 % 0.63 s GPU @ 2.5 Ghz (Python)
216 Det3D 13.26 % 24.00 % 9.94 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
217 PLDet3d 12.85 % 20.72 % 11.11 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
218 DDMP-3D 12.78 % 19.71 % 9.80 % 0.18 s 1 core @ 2.5 Ghz (Python)
219 Kinematic3D code 12.72 % 19.07 % 9.17 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
220 DAMono3D 12.66 % 16.99 % 9.97 % 0.09s 1 core @ 2.5 Ghz (C/C++)
221 RelationNet3D 12.60 % 17.57 % 10.95 % 0.04 s GPU @ 2.5 Ghz (Python)
222 Object Transformer 12.58 % 17.87 % 10.87 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
223 MonoGeo 12.57 % 16.87 % 11.16 % 0.05 s 1 core @ 2.5 Ghz (Python)
224 MTMono3d 12.44 % 18.54 % 10.09 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
225 MonoRUn 12.30 % 19.65 % 10.58 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
226 Deprecated 12.30 % 16.48 % 9.14 % Deprecated Deprecated
227 DLE 12.26 % 17.23 % 10.29 % 0.04 s GPU @ 2.5 Ghz (Python)
228 DP3D 12.24 % 18.84 % 8.96 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
229 YoloMono3D code 12.06 % 18.28 % 8.42 % 0.05 s GPU @ 2.5 Ghz (Python)
230 IAFA 12.01 % 17.81 % 10.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
231 D4LCN code 11.72 % 16.65 % 9.51 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
232 GA-Aug 11.67 % 17.46 % 9.69 % 0.04 s GPU @ 2.5 Ghz (Python)
233 MP-Mono 11.65 % 16.78 % 9.01 % 0.16 s GPU @ 2.5 Ghz (Python)
234 MCA 11.63 % 18.46 % 10.24 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
235 RetinaMono code 11.61 % 16.68 % 9.57 % 0.02 s 1 core @ 2.5 Ghz (Python)
236 PG-MonoNet 11.51 % 15.91 % 9.01 % 0.19 s GPU @ 2.5 Ghz (Python)
237 DA-3Ddet 11.50 % 16.77 % 8.93 % 0.4 s GPU @ 2.5 Ghz (Python)
238 TBD 11.47 % 19.53 % 9.17 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
239 NL_M3D 11.46 % 17.54 % 8.98 % 0.2 s 1 core @ 2.5 Ghz (Python)
240 KM3D code 11.45 % 16.73 % 9.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
241 IMA 11.34 % 16.24 % 9.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
242 CDI3D 11.32 % 15.70 % 9.26 % 0.03 s GPU @ 2.5 Ghz (Python)
243 LAPNet 11.29 % 18.02 % 8.50 % 0.03 s 1 core @ 2.5 Ghz (Python)
244 LNET 11.21 % 12.79 % 9.94 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
245 RefinedMPL 11.14 % 18.09 % 8.94 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
246 UM3D_TUM 11.13 % 15.30 % 9.31 % 0.05 s 1 core @ 2.5 Ghz (Python)
247 PatchNet code 11.12 % 15.68 % 10.17 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
248 AM3D 10.74 % 16.50 % 9.52 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
249 OCM3D 10.44 % 17.48 % 7.87 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
250 RTM3D code 10.34 % 14.41 % 8.77 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
251 MA 10.21 % 14.90 % 8.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
252 MonoPair 9.99 % 13.04 % 8.65 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
253 FADNet code 9.92 % 16.37 % 8.05 % 0.04 s GPU @ >3.5 Ghz (Python)
254 SMOKE code 9.76 % 14.03 % 7.84 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
255 M3D-RPN code 9.71 % 14.76 % 7.42 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
256 modat3D
This is an online method (no batch processing).
9.33 % 12.81 % 7.86 % 0.03 s GPU @ 2.5 Ghz (Python)
257 RelationNet3D_res18 9.31 % 13.37 % 8.29 % 0.04 s GPU @ 2.5 Ghz (Python)
258 Center3D 9.31 % 12.01 % 8.06 % 0.05 s GPU @ 3.5 Ghz (Python)
259 TopNet-HighRes
This method makes use of Velodyne laser scans.
9.28 % 12.67 % 7.95 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
260 LCD3D 9.04 % 13.77 % 7.23 % 0.03 s GPU @ 2.5 Ghz (Python)
261 SSL-RTM3D Res18 8.39 % 12.65 % 7.12 % 0.02 s GPU @ 2.5 Ghz (Python)
262 SS3D 7.68 % 10.78 % 6.51 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
263 anonymous 7.66 % 15.21 % 6.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
264 Mono3D_PLiDAR code 7.50 % 10.76 % 6.10 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
265 MonoPSR code 7.25 % 10.76 % 5.85 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
266 Decoupled-3D 7.02 % 11.08 % 5.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
267 anonymous 6.77 % 13.18 % 5.63 % 1 s 1 core @ 2.5 Ghz (C/C++)
268 VoxelJones code 6.35 % 7.39 % 5.80 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
269 MonoGRNet code 5.74 % 9.61 % 4.25 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
270 A3DODWTDA (image) code 5.27 % 6.88 % 4.45 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
271 MonoFENet 5.14 % 8.35 % 4.10 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
272 TLNet (Stereo)
This method uses stereo information.
code 4.37 % 7.64 % 3.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
273 AACL 4.18 % 5.62 % 3.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
274 CSoR
This method makes use of Velodyne laser scans.
4.06 % 5.61 % 3.17 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
275 Shift R-CNN (mono) code 3.87 % 6.88 % 2.83 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
276 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
277 SparVox3D 3.20 % 5.27 % 2.56 % 0.05 s GPU @ 2.0 Ghz (Python)
278 TopNet-UncEst
This method makes use of Velodyne laser scans.
3.02 % 3.24 % 2.26 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
279 GS3D 2.90 % 4.47 % 2.47 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
280 3D-GCK 2.52 % 3.27 % 2.11 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
281 ROI-10D 2.02 % 4.32 % 1.46 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
282 FQNet 1.51 % 2.77 % 1.01 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
283 3D-SSMFCNN code 1.41 % 1.88 % 1.11 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
284 UDI-mono3D 0.72 % 0.62 % 0.53 % 0.05 s 1 core @ 2.5 Ghz (Python)
285 UDI-mono3D 0.41 % 0.51 % 0.43 % 0.05 s 1 core @ 2.5 Ghz (Python)
286 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
287 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 HIKVISION-AFree 46.88 % 52.75 % 43.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 ADLAB 46.18 % 53.59 % 43.28 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
3 HotSpotNet 45.37 % 53.10 % 41.47 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
4 SAA-PV-RCNN 45.00 % 52.55 % 41.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
5 SIEV-Net 44.80 % 54.00 % 41.11 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
6 HIKVISION-ADLab-HZ 44.78 % 52.09 % 42.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 TANet code 44.34 % 53.72 % 40.49 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
8 3DSSD code 44.27 % 54.64 % 40.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
9 Point-GNN
This method makes use of Velodyne laser scans.
code 43.77 % 51.92 % 40.14 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
10 PP-3D 43.77 % 51.92 % 40.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
11 MVX-Net++ 43.73 % 50.90 % 39.96 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
12 KNN-GCNN 43.57 % 51.82 % 40.02 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
13 F-ConvNet
This method makes use of Velodyne laser scans.
code 43.38 % 52.16 % 38.80 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
14 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 43.35 % 53.10 % 40.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
15 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 43.29 % 52.17 % 40.29 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
16 TBD_IOU2 43.28 % 51.80 % 40.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 VMVS
This method makes use of Velodyne laser scans.
43.27 % 53.44 % 39.51 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
18 AF 43.09 % 50.65 % 39.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
19 STD code 42.47 % 53.29 % 38.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
20 AVOD-FPN
This method makes use of Velodyne laser scans.
code 42.27 % 50.46 % 39.04 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
21 SemanticVoxels 42.19 % 50.90 % 39.52 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
22 F-PointNet
This method makes use of Velodyne laser scans.
code 42.15 % 50.53 % 38.08 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
23 PointPillars
This method makes use of Velodyne laser scans.
code 41.92 % 51.45 % 38.89 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
24 TBD_IOU1 41.65 % 49.00 % 39.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 epBRM
This method makes use of Velodyne laser scans.
code 41.52 % 49.17 % 39.08 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
26 TBD_IOU 41.45 % 48.25 % 39.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
27 GNN-RCNN 41.32 % 47.48 % 38.97 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
28 PointPainting
This method makes use of Velodyne laser scans.
40.97 % 50.32 % 37.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
29 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
40.89 % 46.97 % 38.80 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
30 XView-PartA^2 40.71 % 47.73 % 38.47 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
31 E^2-PV-RCNN 40.47 % 46.61 % 38.60 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
32 FPCR-CNN 40.32 % 48.33 % 37.66 % 0.05 s 1 core @ 2.5 Ghz (Python)
33 Simple3D Net 40.20 % 48.41 % 37.50 % 0.02 s GPU @ 2.5 Ghz (Python)
34 FPC-RCNN 40.13 % 46.41 % 37.84 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
35 SVGA-Net
This method makes use of Velodyne laser scans.
39.88 % 47.59 % 37.57 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
36 anonymous code 39.74 % 46.09 % 37.41 % 0.05s 1 core @ >3.5 Ghz (python)
37 Fast VP-RCNN code 39.65 % 45.95 % 37.29 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
38 PF-GAP 39.53 % 47.63 % 36.44 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
39 TBD 39.48 % 45.46 % 37.35 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
40 GAP-soft-filter 39.47 % 46.93 % 36.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
41 CVRS VIC-RCNN 39.46 % 45.19 % 37.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
42 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
39.43 % 47.30 % 36.99 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
43 Baseline of CA RCNN 39.42 % 47.30 % 36.97 % 0.1 s GPU @ 2.5 Ghz (Python)
44 CVIS-DF3D 39.42 % 47.30 % 36.97 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
45 FSA-PVRCNN
This method makes use of Velodyne laser scans.
39.39 % 44.14 % 37.13 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
46 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 39.37 % 47.98 % 36.01 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
47 ARPNET 39.31 % 48.32 % 35.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
48 TBD 39.31 % 46.85 % 36.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
49 tbd 38.89 % 45.98 % 35.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 SIF 38.74 % 46.23 % 36.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
51 SCNet
This method makes use of Velodyne laser scans.
38.66 % 47.83 % 35.70 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
52 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 38.58 % 46.33 % 35.71 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
53 MSL3D 38.58 % 45.00 % 35.72 % 0.03 s GPU @ 2.5 Ghz (Python)
54 CVIS-DF3D_v2 38.31 % 45.10 % 36.15 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
55 AF_MCLS 38.29 % 47.07 % 34.67 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
56 3DBN_2 38.23 % 46.79 % 35.57 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
57 MKFFNet 38.05 % 46.01 % 35.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
58 IGRP+ 38.05 % 46.26 % 34.53 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
59 FPC3D_all
This method makes use of Velodyne laser scans.
37.95 % 45.49 % 35.60 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
60 VGCN 37.60 % 45.28 % 34.96 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
61 MGACNet 37.50 % 43.55 % 35.33 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
62 MLOD
This method makes use of Velodyne laser scans.
code 37.47 % 47.58 % 35.07 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
63 TBD 37.37 % 43.60 % 34.36 % 0.05 s GPU @ 2.5 Ghz (Python)
64 CVRS VIC-Net 37.18 % 43.82 % 35.35 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
65 XView 36.79 % 42.44 % 34.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 MKFFNet 36.66 % 43.94 % 34.56 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
67 MKFFNet 36.65 % 44.00 % 34.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 PFF3D
This method makes use of Velodyne laser scans.
36.07 % 43.93 % 32.86 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
69 NLK-3D 35.86 % 45.17 % 32.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
70 deprecated 35.21 % 41.32 % 33.32 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
71 RoIFusion code 35.14 % 42.22 % 32.92 % 0.22 s 1 core @ 3.0 Ghz (Python)
72 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 34.59 % 42.27 % 31.37 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
73 NLK-ALL code 34.46 % 44.30 % 30.83 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
74 DAMNET code 33.66 % 43.32 % 30.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
75 CBi-GNN-persons 32.92 % 41.65 % 29.19 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
76 IOU-SSD code 32.57 % 39.15 % 30.24 % 0.045s 1 core @ 2.5 Ghz (C/C++)
77 BirdNet+
This method makes use of Velodyne laser scans.
code 31.46 % 37.99 % 29.46 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
78 Pointpillar_TV 30.79 % 38.56 % 28.57 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
79 SparsePool code 30.38 % 37.84 % 26.94 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
80 FCY
This method makes use of Velodyne laser scans.
29.38 % 37.28 % 26.19 % 0.02 s GPU @ 2.5 Ghz (Python)
81 SparsePool code 27.92 % 35.52 % 25.87 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
82 AVOD
This method makes use of Velodyne laser scans.
code 27.86 % 36.10 % 25.76 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
83 CSW3D
This method makes use of Velodyne laser scans.
26.64 % 33.75 % 23.34 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
84 Disp R-CNN (velo)
This method uses stereo information.
code 25.80 % 37.12 % 22.04 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
85 Disp R-CNN
This method uses stereo information.
code 25.40 % 35.75 % 21.79 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
86 tiny-stereo-v2
This method uses stereo information.
25.13 % 35.02 % 22.36 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
87 CG-Stereo
This method uses stereo information.
24.31 % 33.22 % 20.95 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
88 stereo-tkc
This method uses stereo information.
23.83 % 31.66 % 21.37 % 0.4 s GPU @ 2.0 Ghz (Python + C/C++)
89 tiny-stereo
This method uses stereo information.
23.66 % 32.51 % 21.08 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
90 NCL code 23.33 % 27.75 % 21.66 % NA s 1 core @ 2.5 Ghz (Python)
91 VN3D 21.03 % 26.46 % 18.40 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
92 OSE
This method uses stereo information.
20.65 % 28.68 % 17.65 % 0.1 s GPU @ 2.5 Ghz (C/C++)
93 Stereo3D
This method uses stereo information.
19.75 % 28.49 % 16.48 % 0.1 s GPU 1080Ti
94 OSE+ 19.67 % 28.30 % 17.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
95 OC Stereo
This method uses stereo information.
code 17.58 % 24.48 % 15.60 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
96 BirdNet
This method makes use of Velodyne laser scans.
17.08 % 22.04 % 15.82 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
97 DSGN
This method uses stereo information.
code 15.55 % 20.53 % 14.15 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
98 AEC3D 14.98 % 19.85 % 13.66 % 0.01 s GPU @ 2.5 Ghz (Python)
99 SOD 14.68 % 21.13 % 12.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
100 Complexer-YOLO
This method makes use of Velodyne laser scans.
13.96 % 17.60 % 12.70 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
101 RT3D-GMP
This method uses stereo information.
11.41 % 16.23 % 10.12 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
102 CaDDN 8.14 % 12.87 % 6.76 % 0.63 s GPU @ 2.5 Ghz (Python)
103 RefinedMPL 7.18 % 11.14 % 5.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
104 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.92 % 10.40 % 6.63 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
105 MonoRUn 6.78 % 10.88 % 5.83 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
106 MonoPair 6.68 % 10.02 % 5.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
107 DLE 6.55 % 9.64 % 5.44 % 0.04 s GPU @ 2.5 Ghz (Python)
108 MonoFlex 6.31 % 9.43 % 5.26 % 0.03 s GPU @ 2.5 Ghz (Python)
109 DAMono3D 5.68 % 7.86 % 4.81 % 0.09s 1 core @ 2.5 Ghz (C/C++)
110 Deprecated 5.62 % 7.52 % 4.71 % Deprecated Deprecated
111 RelationNet3D_res18 5.25 % 8.34 % 4.72 % 0.04 s GPU @ 2.5 Ghz (Python)
112 GA-Aug 4.89 % 8.04 % 4.32 % 0.04 s GPU @ 2.5 Ghz (Python)
113 MonoGeo 4.78 % 7.59 % 4.41 % 0.05 s 1 core @ 2.5 Ghz (Python)
114 Shift R-CNN (mono) code 4.66 % 7.95 % 4.16 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
115 PG-MonoNet 4.50 % 5.76 % 3.93 % 0.19 s GPU @ 2.5 Ghz (Python)
116 PLDet3d 4.25 % 6.31 % 3.49 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
117 M3D-RPN(S-R) 4.11 % 5.70 % 3.37 % 0.16 s GPU @ 1.5 Ghz (Python)
118 CDI3D 4.03 % 5.64 % 3.29 % 0.03 s GPU @ 2.5 Ghz (Python)
119 MonoPSR code 4.00 % 6.12 % 3.30 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
120 NL_M3D 3.87 % 5.16 % 3.08 % 0.2 s 1 core @ 2.5 Ghz (Python)
121 MP-Mono 3.79 % 5.30 % 3.15 % 0.16 s GPU @ 2.5 Ghz (Python)
122 DDMP-3D 3.55 % 4.93 % 3.01 % 0.18 s 1 core @ 2.5 Ghz (Python)
123 FADNet code 3.53 % 5.40 % 3.31 % 0.04 s GPU @ >3.5 Ghz (Python)
124 M3D-RPN code 3.48 % 4.92 % 2.94 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
125 Center3D 3.43 % 4.86 % 2.78 % 0.05 s GPU @ 3.5 Ghz (Python)
126 D4LCN code 3.42 % 4.55 % 2.83 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
127 modat3D
This is an online method (no batch processing).
3.37 % 5.53 % 3.02 % 0.03 s GPU @ 2.5 Ghz (Python)
128 DP3D 3.37 % 4.77 % 2.77 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
129 LAPNet 3.16 % 4.41 % 2.70 % 0.03 s 1 core @ 2.5 Ghz (Python)
130 MonoEF code 2.79 % 4.27 % 2.21 % 0.03 s 1 core @ 2.5 Ghz (Python)
131 RT3DStereo
This method uses stereo information.
2.45 % 3.28 % 2.35 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
132 MTMono3d 2.05 % 2.40 % 1.68 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
133 TopNet-UncEst
This method makes use of Velodyne laser scans.
1.87 % 3.42 % 1.73 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
134 SS3D 1.78 % 2.31 % 1.48 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
135 UM3D_TUM 1.74 % 3.49 % 1.74 % 0.05 s 1 core @ 2.5 Ghz (Python)
136 UDI-mono3D 1.45 % 2.18 % 1.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
137 SparVox3D 1.35 % 1.93 % 1.04 % 0.05 s GPU @ 2.0 Ghz (Python)
138 UDI-mono3D 1.01 % 1.81 % 0.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
139 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
140 TBD 0.00 % 0.00 % 0.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
141 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 HRI-MSP-L
This method makes use of Velodyne laser scans.
71.86 % 87.77 % 63.57 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
2 HIKVISION-AFree 69.92 % 84.65 % 63.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 anonymous code 69.13 % 83.09 % 61.35 % 0.05s 1 core @ >3.5 Ghz (python)
4 Fast VP-RCNN code 69.02 % 83.81 % 61.51 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
5 SAA-PV-RCNN 68.96 % 82.06 % 61.54 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
6 HIKVISION-ADLab-HZ 68.83 % 84.82 % 60.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
68.54 % 82.19 % 61.33 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
8 E^2-PV-RCNN 68.03 % 81.55 % 60.51 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
9 RangeIoUDet
This method makes use of Velodyne laser scans.
67.77 % 83.12 % 60.26 % 0.02 s 1 core @ 2.5 Ghz (Python)
10 FPC-RCNN 67.57 % 82.79 % 60.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
11 GNN-RCNN 67.49 % 81.25 % 61.15 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
12 PV-RCNN-v2 67.33 % 82.22 % 60.04 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
13 CBi-GNN-persons 66.49 % 79.95 % 59.12 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
14 XView-PartA^2 66.33 % 80.65 % 59.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
15 HotSpotNet 65.95 % 82.59 % 59.00 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
16 TBD 65.64 % 82.29 % 57.98 % 0.05 s GPU @ 2.5 Ghz (Python)
17 TBD 65.64 % 82.29 % 57.98 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
18 FSA-PVRCNN
This method makes use of Velodyne laser scans.
65.20 % 80.68 % 59.14 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
19 F-ConvNet
This method makes use of Velodyne laser scans.
code 65.07 % 81.98 % 56.54 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
20 CVRS VIC-RCNN 64.99 % 81.47 % 58.62 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
21 TBD 64.60 % 80.49 % 57.18 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
22 3DSSD code 64.10 % 82.48 % 56.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
23 RoIFusion code 64.05 % 80.84 % 58.37 % 0.22 s 1 core @ 3.0 Ghz (Python)
24 PointPainting
This method makes use of Velodyne laser scans.
63.78 % 77.63 % 55.89 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
25 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 63.71 % 78.60 % 57.65 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
26 TBD_IOU 63.68 % 79.74 % 56.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
27 CVRS VIC-Net 63.65 % 78.29 % 57.27 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
28 NLK-ALL code 63.65 % 79.94 % 57.28 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
29 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 63.52 % 79.17 % 56.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
30 Point-GNN
This method makes use of Velodyne laser scans.
code 63.48 % 78.60 % 57.08 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
31 PP-3D 63.48 % 78.60 % 57.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
32 AF 63.43 % 80.64 % 55.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
33 TBD_IOU2 63.41 % 81.49 % 56.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
34 CVIS-DF3D_v2 63.05 % 77.46 % 55.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
35 KNN-GCNN 62.91 % 80.24 % 56.49 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
36 tbd 62.75 % 78.45 % 56.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 H^23D R-CNN 62.74 % 78.67 % 55.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
38 TBD_IOU1 62.67 % 80.32 % 55.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
39 FPCR-CNN 62.56 % 79.61 % 55.82 % 0.05 s 1 core @ 2.5 Ghz (Python)
40 VGCN 62.36 % 78.47 % 55.88 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
41 MSL3D 62.27 % 76.74 % 56.20 % 0.03 s GPU @ 2.5 Ghz (Python)
42 deprecated 62.16 % 75.45 % 56.00 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
43 GAP-soft-filter 62.04 % 77.06 % 55.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
44 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
62.02 % 77.35 % 55.52 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
45 Baseline of CA RCNN 62.02 % 77.33 % 55.52 % 0.1 s GPU @ 2.5 Ghz (Python)
46 CVIS-DF3D 62.02 % 77.33 % 55.52 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
47 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 62.00 % 77.36 % 55.40 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
48 MGACNet 62.00 % 78.73 % 55.18 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
49 SVGA-Net
This method makes use of Velodyne laser scans.
61.86 % 75.45 % 54.68 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
50 MKFFNet 61.80 % 78.08 % 54.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
51 SIF 61.61 % 77.13 % 55.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 STD code 61.59 % 78.69 % 55.30 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
53 MVX-Net++ 61.03 % 76.07 % 53.41 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
54 AF_MCLS 60.89 % 78.82 % 54.13 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
55 3DBN_2 60.88 % 78.10 % 54.10 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
56 MKFFNet 60.48 % 76.68 % 54.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 60.30 % 75.42 % 53.81 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
58 CCFNET 60.18 % 78.05 % 53.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
59 SIEV-Net 59.99 % 78.75 % 52.37 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
60 PF-GAP 59.92 % 77.88 % 53.48 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
61 XView 59.55 % 77.24 % 53.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 FCY
This method makes use of Velodyne laser scans.
59.54 % 76.30 % 52.29 % 0.02 s GPU @ 2.5 Ghz (Python)
63 FPC3D_all
This method makes use of Velodyne laser scans.
59.45 % 74.75 % 52.71 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
64 TANet code 59.44 % 75.70 % 52.53 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
65 NLK-3D 59.30 % 76.45 % 51.82 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
66 MKFFNet 59.14 % 75.64 % 52.97 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
67 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 58.82 % 74.96 % 52.53 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
68 PointPillars
This method makes use of Velodyne laser scans.
code 58.65 % 77.10 % 51.92 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
69 ARPNET 58.20 % 74.21 % 52.13 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
70 IOU-SSD code 57.65 % 71.77 % 52.31 % 0.045s 1 core @ 2.5 Ghz (C/C++)
71 epBRM
This method makes use of Velodyne laser scans.
code 56.13 % 72.08 % 49.91 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
72 F-PointNet
This method makes use of Velodyne laser scans.
code 56.12 % 72.27 % 49.01 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
73 Pointpillar_TV 54.69 % 71.61 % 48.22 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
74 Simple3D Net 54.49 % 70.79 % 48.21 % 0.02 s GPU @ 2.5 Ghz (Python)
75 IGRP+ 53.22 % 69.87 % 47.55 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
76 SCNet
This method makes use of Velodyne laser scans.
50.79 % 67.98 % 45.15 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
77 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.55 % 63.76 % 44.93 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
78 MLOD
This method makes use of Velodyne laser scans.
code 49.43 % 68.81 % 42.84 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
79 BirdNet+
This method makes use of Velodyne laser scans.
code 47.72 % 67.38 % 42.89 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
80 PFF3D
This method makes use of Velodyne laser scans.
46.78 % 63.27 % 41.37 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
81 DAMNET code 42.82 % 58.71 % 38.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
82 AVOD
This method makes use of Velodyne laser scans.
code 42.08 % 57.19 % 38.29 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
83 SparsePool code 37.33 % 52.61 % 33.39 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
84 stereo-tkc
This method uses stereo information.
34.53 % 51.87 % 30.01 % 0.4 s GPU @ 2.0 Ghz (Python + C/C++)
85 SparsePool code 32.61 % 40.87 % 29.05 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
86 tiny-stereo-v2
This method uses stereo information.
31.31 % 46.37 % 27.66 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
87 CG-Stereo
This method uses stereo information.
30.89 % 47.40 % 27.23 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
88 BirdNet
This method makes use of Velodyne laser scans.
30.25 % 43.98 % 27.21 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
89 tiny-stereo
This method uses stereo information.
27.76 % 42.80 % 24.10 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
90 SOD 25.29 % 40.51 % 21.32 % 0.1 s 1 core @ 2.5 Ghz (Python)
91 Disp R-CNN (velo)
This method uses stereo information.
code 24.40 % 40.05 % 21.12 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
92 Disp R-CNN
This method uses stereo information.
code 24.40 % 40.04 % 21.12 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
93 OSE+ 20.75 % 32.62 % 17.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
94 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.53 % 24.27 % 17.31 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
95 DSGN
This method uses stereo information.
code 18.17 % 27.76 % 16.21 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
96 VN3D 17.46 % 24.83 % 16.73 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
97 OSE
This method uses stereo information.
17.28 % 28.50 % 15.56 % 0.1 s GPU @ 2.5 Ghz (C/C++)
98 OC Stereo
This method uses stereo information.
code 16.63 % 29.40 % 14.72 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
99 RT3D-GMP
This method uses stereo information.
12.99 % 18.31 % 10.63 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
100 AEC3D 10.76 % 18.08 % 9.99 % 0.01 s GPU @ 2.5 Ghz (Python)
101 MonoPSR code 4.74 % 8.37 % 3.68 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
102 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.54 % 7.13 % 3.81 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
103 DAMono3D 3.80 % 5.65 % 3.23 % 0.09s 1 core @ 2.5 Ghz (C/C++)
104 CaDDN 3.41 % 7.00 % 3.30 % 0.63 s GPU @ 2.5 Ghz (Python)
105 RT3DStereo
This method uses stereo information.
3.37 % 5.29 % 2.57 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
106 MonoGeo 3.05 % 4.74 % 2.69 % 0.05 s 1 core @ 2.5 Ghz (Python)
107 Deprecated 2.71 % 3.89 % 2.27 % Deprecated Deprecated
108 CDI3D 2.69 % 4.15 % 2.45 % 0.03 s GPU @ 2.5 Ghz (Python)
109 DLE 2.66 % 4.59 % 2.45 % 0.04 s GPU @ 2.5 Ghz (Python)
110 DDMP-3D 2.50 % 4.18 % 2.32 % 0.18 s 1 core @ 2.5 Ghz (Python)
111 modat3D
This is an online method (no batch processing).
2.39 % 4.16 % 1.85 % 0.03 s GPU @ 2.5 Ghz (Python)
112 Center3D 2.35 % 4.32 % 2.06 % 0.05 s GPU @ 3.5 Ghz (Python)
113 MonoFlex 2.35 % 4.17 % 2.04 % 0.03 s GPU @ 2.5 Ghz (Python)
114 RelationNet3D_res18 2.33 % 4.51 % 2.22 % 0.04 s GPU @ 2.5 Ghz (Python)
115 MonoPair 2.12 % 3.79 % 1.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
116 RefinedMPL 1.82 % 3.23 % 1.77 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
117 UDI-mono3D 1.74 % 3.29 % 1.37 % 0.05 s 1 core @ 2.5 Ghz (Python)
118 TopNet-HighRes
This method makes use of Velodyne laser scans.
1.67 % 2.49 % 1.88 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
119 D4LCN code 1.67 % 2.45 % 1.36 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
120 DP3D 1.66 % 2.77 % 1.31 % 0.07 s GPU @ 1.5 Ghz (Python + C/C++)
121 NL_M3D 1.51 % 2.10 % 1.58 % 0.2 s 1 core @ 2.5 Ghz (Python)
122 UDI-mono3D 1.47 % 3.01 % 1.47 % 0.05 s 1 core @ 2.5 Ghz (Python)
123 SS3D 1.45 % 2.80 % 1.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
124 PG-MonoNet 1.43 % 2.41 % 1.23 % 0.19 s GPU @ 2.5 Ghz (Python)
125 MP-Mono 1.42 % 1.89 % 1.29 % 0.16 s GPU @ 2.5 Ghz (Python)
126 GA-Aug 1.20 % 1.99 % 1.09 % 0.04 s GPU @ 2.5 Ghz (Python)
127 MonoEF code 0.92 % 1.80 % 0.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
128 MTMono3d 0.90 % 1.59 % 0.96 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
129 LAPNet 0.89 % 1.37 % 0.62 % 0.03 s 1 core @ 2.5 Ghz (Python)
130 PLDet3d 0.80 % 1.24 % 0.89 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
131 M3D-RPN code 0.65 % 0.94 % 0.47 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
132 FADNet code 0.64 % 1.44 % 0.67 % 0.04 s GPU @ >3.5 Ghz (Python)
133 UM3D_TUM 0.62 % 0.45 % 0.62 % 0.05 s 1 core @ 2.5 Ghz (Python)
134 MonoRUn 0.61 % 1.01 % 0.48 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
135 Shift R-CNN (mono) code 0.29 % 0.48 % 0.31 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
136 PVNet 0.00 % 0.00 % 0.00 % 0,1 s 1 core @ 2.5 Ghz (Python)
137 TBD 0.00 % 0.00 % 0.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
138 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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