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 NFAF3D 91.97 % 95.72 % 86.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
2 VPFNet 91.86 % 93.02 % 86.94 % 0.06 s 2 cores @ 2.5 Ghz (Python)
3 SE-SSD
This method makes use of Velodyne laser scans.
code 91.84 % 95.68 % 86.72 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
4 CityBrainLab 91.60 % 94.75 % 86.67 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
5 SPANet 91.59 % 95.59 % 86.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
6 Anonymous 91.53 % 95.04 % 86.69 % 0.1 s GPU @ 2.5 Ghz (Python)
7 SFD 91.38 % 95.23 % 86.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 BANet code 91.32 % 95.23 % 86.48 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: Boundary-Aware 3D Object Detection from Point Clouds. 2021.
9 Anonymous 91.04 % 94.31 % 86.31 % 0.1s 1 core @ 2.5 Ghz (C/C++)
10 SFD_NEW 91.04 % 94.76 % 86.31 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
11 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.
12 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.
13 VueronNet code 90.56 % 94.67 % 85.31 % 0.06 s 1 core @ 2.0 Ghz (Python)
14 ST-RCNN
This method makes use of Velodyne laser scans.
90.53 % 94.58 % 86.08 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
15 ST-RCNN (SNLW-RCNN)
This method makes use of Velodyne laser scans.
code 90.53 % 94.58 % 86.08 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
16 VPFNet code 90.52 % 93.94 % 86.25 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
17 SARFE 90.45 % 94.39 % 85.97 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
18 HyBrid Feature Det 90.35 % 92.87 % 85.87 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
19 VoTr-TSD 90.34 % 94.03 % 86.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
20 TransCyclistNet 90.33 % 92.68 % 85.90 % 0.08 s 1 core @ 2.5 Ghz (Python)
21 Fast VP-RCNN code 90.32 % 95.09 % 85.84 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
22 WHUT-iou_ssd code 90.31 % 94.22 % 85.83 % 0.045s 1 core @ 2.5 Ghz (C/C++)
23 LZY_RCNN 90.29 % 92.88 % 85.84 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
24 E^2-PV-RCNN 90.27 % 92.51 % 86.01 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
25 anonymous code 90.22 % 94.86 % 85.73 % 0.05s 1 core @ >3.5 Ghz (python)
26 MSG-PGNN 90.20 % 92.89 % 85.80 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
27 DSA-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, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
28 XView 90.12 % 92.27 % 85.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
29 Generalized-SIENet 90.09 % 92.12 % 85.88 % 0.08 s 1 core @ 2.5 Ghz (Python)
30 SCIR-Net
This method makes use of Velodyne laser scans.
90.04 % 92.11 % 85.63 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
31 FPC-RCNN 90.03 % 92.74 % 85.67 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
32 TPCG 90.02 % 92.22 % 85.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
33 Associate-3Ddet_v2 90.00 % 95.55 % 84.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
34 TransDet3D 89.98 % 92.44 % 85.71 % 0.08 s 1 core @ 2.5 Ghz (Python)
35 SAA-PV-RCNN 89.88 % 91.54 % 86.93 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
36 FSA-PVRCNN
This method makes use of Velodyne laser scans.
89.87 % 92.30 % 85.71 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
37 EBM3DOD code 89.86 % 95.64 % 84.56 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
38 CIA-SSD
This method makes use of Velodyne laser scans.
code 89.84 % 93.74 % 82.39 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
39 HIKVISION-ADLab-HZ 89.83 % 93.21 % 84.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 CLOCs_PVCas code 89.80 % 93.05 % 86.57 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
41 CM3DV 89.77 % 95.54 % 84.49 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
42 NFAF3D-light 89.76 % 95.41 % 86.42 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
43 EA-M-RCNN(BorderAtt) 89.76 % 94.67 % 86.73 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
44 sa-voxel-centernet code 89.74 % 92.02 % 85.69 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
45 AM-SSD 89.74 % 95.56 % 84.65 % 0.04 s 1 core @ 2.5 Ghz (Python)
46 CBi-GNN 89.74 % 95.92 % 84.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
47 EBM3DOD baseline code 89.63 % 95.44 % 84.34 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
48 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.
49 Struc info fusion II 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: Struc info fusion. Submitted to CVIU 2021.
50 Fast 89.49 % 93.03 % 86.40 % 0.1 s GPU @ 2.5 Ghz (Python)
51 KpNet 89.49 % 93.29 % 81.92 % 42 s 1 core @ 2.5 Ghz (C/C++)
52 IA-SSD (single) 89.48 % 93.14 % 84.42 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
53 Seg-RCNN code 89.39 % 93.36 % 81.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
54 Struc info fusion I 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: Struc info fusion. Submitted to CVIU 2021.
55 JPVNet 89.36 % 92.78 % 84.37 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
56 PLNL-3DSSD
This method makes use of Velodyne laser scans.
89.36 % 93.00 % 84.18 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
57 ASCNet 89.36 % 92.85 % 86.45 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
58 BtcDet
This method makes use of Velodyne laser scans.
89.34 % 92.81 % 84.55 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
59 IA-SSD (multi) 89.33 % 92.79 % 84.35 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
60 PPAF
This method makes use of Velodyne laser scans.
89.29 % 92.94 % 84.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
61 PSS 89.28 % 93.17 % 84.38 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
62 GNN-RCNN 89.28 % 92.13 % 85.49 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
63 TBD 89.21 % 92.74 % 84.23 % TBD GPU @ 2.5 Ghz (Python + C/C++)
64 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.
65 MBDF-Net 89.18 % 95.36 % 84.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 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.
67 Anonymous 89.15 % 92.47 % 85.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
68 SGNet 89.14 % 93.04 % 86.54 % 0.09 s GPU @ 2.5 Ghz (Python)
69 SPG_mini
This method makes use of Velodyne laser scans.
89.12 % 92.80 % 86.27 % 0.09 s GPU @ 2.5 Ghz (Python)
70 TBD 89.11 % 92.42 % 84.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
71 RoIFusion code 89.03 % 92.88 % 83.94 % 0.22 s 1 core @ 3.0 Ghz (Python)
72 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.
73 EPNet++ 89.00 % 95.41 % 85.73 % 0.1 s GPU @ 2.5 Ghz (Python)
74 SECOND 88.98 % 92.01 % 83.67 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
75 ISE-RCNN 88.97 % 92.86 % 86.28 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
76 Sem-Aug v1 code 88.92 % 92.59 % 84.29 % 0.04 s GPU @ 3.5 Ghz (Python)
77 MBDF-Net-1 88.90 % 94.68 % 83.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
78 VCT 88.90 % 93.01 % 84.23 % 0.2 s 1 core @ 2.5 Ghz (Python)
79 H^23D R-CNN code 88.87 % 92.85 % 86.07 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
80 MVOD 88.85 % 92.50 % 86.19 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
81 Pyramid R-CNN 88.84 % 92.19 % 86.21 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
82 TBD 88.83 % 92.96 % 86.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 CityBrainLab-CT3D code 88.83 % 92.36 % 84.07 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
84 Voxel R-CNN code 88.83 % 94.85 % 86.13 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
85 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.
86 SRIF-RCNN 88.77 % 92.10 % 86.06 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
87 TBD 88.75 % 92.30 % 84.01 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
88 PV-RCNN-v2 88.74 % 92.66 % 85.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
89 SPG
This method makes use of Velodyne laser scans.
code 88.70 % 94.33 % 85.98 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
90 ISE-RCNN-PV 88.69 % 92.31 % 86.10 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
91 SIENet code 88.65 % 92.38 % 86.03 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
92 P2V-RCNN 88.63 % 92.72 % 86.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
93 demo 88.62 % 92.35 % 83.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
94 FromVoxelToPoint code 88.61 % 92.23 % 86.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
95 RangeIoUDet
This method makes use of Velodyne laser scans.
88.59 % 92.28 % 85.83 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
96 SIEV-Net 88.58 % 92.27 % 83.36 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
97 ISF-v2 88.57 % 92.15 % 83.91 % 0.04 s 1 core @ 2.5 Ghz (Python)
98 SqueezeRCNN 88.52 % 92.65 % 85.82 % 0.08 s 1 core @ 2.5 Ghz (Python)
99 FusionDetv2-v3 88.47 % 92.55 % 85.63 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
100 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.
101 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.
102 FrustumRCNN 88.44 % 92.00 % 85.90 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
103 PC-RGNN 88.43 % 92.08 % 85.81 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
104 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.
105 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.
106 Point Image Fusion 88.39 % 92.14 % 85.78 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
107 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.
108 StructuralIF 88.38 % 91.78 % 85.67 % 0.02 s 8 cores @ 2.5 Ghz (Python)
109 CSVoxel-RCNN 88.38 % 92.09 % 85.59 % 0.03 s GPU @ 1.0 Ghz (Python)
110 PF-GAP 88.35 % 92.16 % 85.64 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
111 ReFineNet 88.32 % 91.93 % 85.68 % 0.08 s 1 core @ 2.5 Ghz (Python)
112 SA-voxel-centernet code 88.28 % 91.80 % 85.73 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
113 FusionDetv2-v4 88.27 % 92.05 % 85.38 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
114 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.
115 MSL3D 88.23 % 91.64 % 85.53 % 0.03 s GPU @ 2.5 Ghz (Python)
116 Multi-Sensor3D 88.23 % 91.64 % 85.53 % 0.03 s GPU @ 2.5 Ghz (Python)
117 3DIoU_v2 88.22 % 92.52 % 85.90 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
118 SVGA-Net
This method makes use of Velodyne laser scans.
88.21 % 91.98 % 85.46 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
119 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.
120 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.
121 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
88.17 % 92.01 % 85.43 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
122 3DIoU++ 88.16 % 91.79 % 85.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 FPC3D
This method makes use of the epipolar geometry.
88.15 % 91.92 % 85.32 % 33 s 1 core @ 2.5 Ghz (C/C++)
124 3D-VDNet 88.15 % 91.72 % 84.65 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
125 SAA-SECOND 88.14 % 91.32 % 85.23 % 38m s 1 core @ 2.5 Ghz (C/C++)
126 FusionDetv1 88.13 % 91.91 % 85.40 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
127 FPCR-CNN 88.12 % 92.62 % 85.18 % 0.05 s 1 core @ 2.5 Ghz (Python)
128 GAP-soft-filter 88.11 % 91.88 % 85.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 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.
130 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.
131 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.
132 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.
133 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 88.08 % 91.90 % 85.35 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
134 NV2P-RCNN 88.08 % 93.44 % 85.32 % 0.1 s GPU @ 2.5 Ghz (Python)
135 VFN 88.06 % 91.23 % 83.26 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
136 FusionDetv2-v2 88.04 % 91.77 % 85.29 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
137 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.
138 CCFNET 87.97 % 94.25 % 83.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
139 HVPR 87.94 % 91.76 % 83.03 % 0.02 s GPU @ 2.5 Ghz (Python)
140 AIMC-RUC 87.91 % 93.92 % 82.70 % 0.11 s 1 core @ 2.5 Ghz (Python)
141 TBD 87.89 % 91.39 % 85.24 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
142 CVFNet 87.87 % 93.65 % 82.29 % 28.1ms 1 core @ 2.5 Ghz (Python)
143 FusionDetv2-v5 87.86 % 91.92 % 83.07 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
144 XView-PartA^2 87.84 % 91.94 % 85.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
145 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.
146 TBD 87.83 % 91.80 % 85.19 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
147 YF 87.81 % 92.11 % 83.07 % 0.04 s GPU @ 2.5 Ghz (C/C++)
148 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.
149 MKFFNet 87.78 % 91.85 % 84.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
150 FPGNN 87.78 % 92.21 % 80.86 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
151 SIF 87.76 % 91.44 % 85.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
152 MVAF-Net code 87.73 % 91.95 % 85.00 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
153 DVFENet 87.68 % 90.93 % 84.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
154 S-AT GCN 87.68 % 90.85 % 84.20 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
155 AutoAlign 87.60 % 91.72 % 84.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
156 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.
157 VOXEL_3D 87.55 % 90.83 % 82.24 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
158 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.
159 V3D 87.53 % 90.83 % 82.30 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
160 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.
161 MGAF-3DSSD code 87.47 % 92.70 % 82.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
162 MKFFNet 87.41 % 91.62 % 84.67 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
163 MKFFNet 87.41 % 91.93 % 84.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
164 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.
165 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.
166 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.
167 DPointNet 87.29 % 88.96 % 82.61 % 0.07s 1 core @ 2.5 Ghz (C/C++)
168 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.
169 VGCN 87.16 % 90.67 % 82.98 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
170 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.
171 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.
172 sscl-20p 86.82 % 91.43 % 82.06 % 0.02 s 1 core @ 2.5 Ghz (Python)
173 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.
174 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.
175 HNet-3DSSD
This method makes use of Velodyne laser scans.
code 86.69 % 91.65 % 81.05 % 0.05 s GPU @ 2.5 Ghz (Python)
176 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.
177 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.
178 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.
179 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.
180 IGRP+ 86.29 % 92.20 % 81.48 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
181 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.
182 APL-Second 86.16 % 91.45 % 81.08 % 0.05 s 1 core @ 2.5 Ghz (Python)
183 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.
184 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.
185 TBD 85.91 % 90.88 % 80.95 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
186 LSNet 85.89 % 92.12 % 80.80 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
187 DASS 85.85 % 91.74 % 80.97 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. 2020.
188 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.
189 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.
190 PointRGBNet 85.73 % 91.39 % 80.68 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
191 Sem-Aug-PointRCNN code 85.50 % 89.75 % 83.13 % 0.1 s GPU @ 3.5 Ghz (C/C++)
192 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.
193 PFF3D
This method makes use of Velodyne laser scans.
code 85.08 % 89.61 % 80.42 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
194 RangeDet code 85.06 % 89.88 % 80.23 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
195 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.
196 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.
197 FPC3D_all
This method makes use of Velodyne laser scans.
84.85 % 91.05 % 80.23 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
198 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.
199 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.
200 FusionDetv2-v1 84.45 % 89.64 % 79.73 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
201 FusionDetv2-baseline 84.31 % 90.38 % 79.23 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
202 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.
203 KMC code 83.90 % 88.87 % 76.87 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
204 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.
205 HS3D code 82.00 % 87.40 % 74.95 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
206 BirdNet+
This method makes use of Velodyne laser scans.
code 81.85 % 87.43 % 75.36 % 0.11 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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
207 AEC3D 80.37 % 86.81 % 74.26 % 18 ms GPU @ 2.5 Ghz (Python)
208 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.
209 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.
210 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.
211 VN3D 77.45 % 86.35 % 71.59 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
212 MMLAB LIGA-Stereo
This method uses stereo information.
code 76.78 % 88.15 % 67.40 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
213 R-AGNO-Net 76.24 % 80.10 % 70.38 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
214 RCD 75.83 % 82.26 % 69.61 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
215 LIGA-Stereo-old
This method uses stereo information.
74.76 % 88.33 % 65.31 % 0.375 s Titan Xp
216 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.
217 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.
218 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.
219 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.
220 deleted 68.87 % 86.89 % 59.95 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
221 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.
222 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.
223 PLUME
This method uses stereo information.
66.27 % 82.97 % 56.70 % 0.15 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Yang, R. Hu, M. Liang and R. Urtasun: PLUME: Efficient 3D Object Detection from Stereo Images. IROS 2021.
224 CDN
This method uses stereo information.
code 66.24 % 83.32 % 57.65 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
225 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.
226 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.
227 BirdNet+ (legacy)
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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
228 BEVC 61.89 % 69.00 % 56.32 % 35ms GPU @ 1.5 Ghz (Python)
229 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.
230 CDN-PL++
This method uses stereo information.
61.04 % 81.27 % 52.84 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
231 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.
232 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.
233 RT3D-GMP
This method uses stereo information.
59.00 % 69.14 % 45.49 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
234 OSE+ 58.65 % 79.80 % 50.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
235 Disp R-CNN (velo)
This method uses stereo information.
code 58.62 % 79.76 % 47.73 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
236 SOD 58.50 % 81.25 % 49.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
237 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.
238 Disp R-CNN
This method uses stereo information.
code 57.98 % 79.61 % 47.09 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
239 NCL code 57.66 % 50.87 % 57.99 % NA s 1 core @ 2.5 Ghz (Python)
240 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.
241 TBD 54.68 % 65.38 % 48.59 % 0.1 s 1 core @ 2.5 Ghz (Python)
242 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.
243 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.
244 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.
245 TBD 50.41 % 61.32 % 44.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
246 YOLOStereo3D
This method uses stereo information.
code 50.28 % 76.10 % 36.86 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
247 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.
248 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.
249 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.
250 SC(DLA34)
This method uses stereo information.
42.12 % 62.97 % 35.37 % 0.04 s GPU @ 2.5 Ghz (Python)
251 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.
252 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.
253 Digging_M3D 28.84 % 39.74 % 26.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
254 Mobile Stereo R-CNN
This method uses stereo information.
28.78 % 44.51 % 22.30 % 1.8 s NVIDIA Jetson TX2
255 LPCG-Monoflex 24.81 % 35.96 % 21.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
256 SCSTSV-MonoFlex 23.71 % 34.59 % 20.41 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
257 CD-KD 23.10 % 33.69 % 20.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
258 DD3D code 22.56 % 30.98 % 20.03 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
259 MonoCon 22.10 % 31.12 % 19.00 % 0.02 s GPU @ 2.5 Ghz (Python)
260 CA3D 20.77 % 29.57 % 17.88 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
261 MM 20.68 % 29.92 % 17.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
262 vadin-TBD 20.68 % 29.60 % 17.81 % 0.04 s 1 core @ 2.5 Ghz (Python)
263 MonoFlex 20.67 % 30.95 % 17.72 % 0.03 s 1 core @ 2.5 Ghz (Python)
264 LPCG-M3D 20.17 % 30.72 % 16.76 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
265 AutoShape code 20.08 % 30.66 % 15.95 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
266 M3DSSD++ 20.03 % 32.18 % 16.47 % 0.16s 1 core @ 2.5 Ghz (C/C++)
267 MAOLoss code 19.95 % 28.29 % 16.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
268 TBD 19.75 % 27.98 % 17.32 % TBD TBD
269 MonoFlex 19.75 % 28.23 % 16.89 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
270 MonoEF code 19.70 % 29.03 % 17.26 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
271 mono3d 19.67 % 28.15 % 16.73 % 0.03 s GPU @ 2.5 Ghz (Python)
272 K3D 19.60 % 28.31 % 17.62 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
273 ITS-MDPL 19.54 % 33.02 % 17.56 % 0.16 s GPU @ 2.5 Ghz (Python)
274 DFR-Net 19.17 % 28.17 % 14.84 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
275 SwinMono3D 19.15 % 29.65 % 14.09 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
276 GAC3D++ 19.05 % 26.94 % 16.48 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
277 PCT 19.03 % 29.65 % 15.92 % 0.045 s 1 core @ 2.5 Ghz (C/C++)
278 E2E-DA 19.03 % 27.41 % 16.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
279 MonoGeo 18.99 % 25.86 % 16.19 % 0.05 s 1 core @ 2.5 Ghz (Python)
280 CaDDN code 18.91 % 27.94 % 17.19 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
281 monodle code 18.89 % 24.79 % 16.00 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
282 Object Transformer 18.78 % 26.43 % 15.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
283 MonoLCD 18.68 % 25.89 % 16.30 % 0.04 s 1 core @ 2.5 Ghz (Python)
284 none 18.66 % 26.19 % 15.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
285 Neighbor-Vote 18.65 % 27.39 % 16.54 % 0.1 s GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
286 MDSNet 18.65 % 30.92 % 14.53 % 0.07 s 1 core @ 2.5 Ghz (Python)
287 PLDet3d 18.55 % 29.14 % 15.73 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
288 GrooMeD-NMS code 18.27 % 26.19 % 14.05 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
289 AutoShape 18.12 % 28.25 % 14.84 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
290 PPTrans 18.12 % 28.05 % 15.41 % 0.2 s GPU @ 2.5 Ghz (Python)
291 MonoRCNN code 18.11 % 25.48 % 14.10 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
292 Ground-Aware code 17.98 % 29.81 % 13.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
293 MP-Mono 17.96 % 25.36 % 13.84 % 0.16 s GPU @ 2.5 Ghz (Python)
294 Aug3D-RPN 17.89 % 26.00 % 14.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
295 DDMP-3D 17.89 % 28.08 % 13.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
296 IAFA 17.88 % 25.88 % 15.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
297 RelationNet3D_dla34 code 17.74 % 24.27 % 15.38 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
298 TBD 17.70 % 29.97 % 15.04 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
299 RelationNet3D 17.66 % 25.56 % 15.52 % 0.04 s GPU @ 2.5 Ghz (Python)
300 MonoHMOO 17.60 % 27.39 % 13.25 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
301 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.
302 Lite-FPN 17.58 % 26.67 % 14.61 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
303 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 .
304 MonoRUn code 17.34 % 27.94 % 15.24 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
305 RetinaMono 17.33 % 26.12 % 15.26 % 0.02 s 1 core @ 2.5 Ghz (Python)
306 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.
307 Deprecated 17.22 % 23.59 % 13.34 % Deprecated Deprecated
308 DA-Mono3D 17.17 % 23.73 % 13.46 % 0.09s 1 core @ 2.5 Ghz (C/C++)
309 YoloMono3D code 17.15 % 26.79 % 12.56 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
310 GAC3D 16.93 % 25.80 % 12.50 % 0.25 s 1 core @ 2.5 Ghz (Python)
311 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.
312 RetinaMono code 16.85 % 24.52 % 14.02 % 0.02 s 1 core @ 2.5 Ghz (Python)
313 ImVoxelNet code 16.37 % 25.19 % 13.58 % 0.2 s GPU @ 2.5 Ghz (Python)
314 TBD 16.22 % 24.21 % 14.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
315 KM3D code 16.20 % 23.44 % 14.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
316 E2E-DA-Lite (Res18) 16.06 % 23.49 % 13.55 % 0.01 s GPU @ 2.5 Ghz (Python)
317 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.
318 LAPNet 15.76 % 25.10 % 12.30 % 0.03 s 1 core @ 2.5 Ghz (Python)
319 COF3D 15.39 % 25.36 % 11.34 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
320 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.
321 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.
322 QD-3DT
This is an online method (no batch processing).
code 14.71 % 20.16 % 12.76 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
323 RelationNet3D_res18 code 14.59 % 20.54 % 12.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
324 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.
325 ICCV 14.30 % 19.93 % 12.37 % 0.04 s GPU @ 2.5 Ghz (Python)
326 FADNet code 14.22 % 23.00 % 12.56 % 0.04 s GPU @ >3.5 Ghz (Python)
327 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.
328 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.
329 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 .
330 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.
331 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.
332 Geo3D 11.86 % 16.31 % 10.26 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
333 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.
334 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.
335 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.
336 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.
337 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.
338 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.
339 SparVox3D 6.39 % 10.20 % 5.06 % 0.05 s GPU @ 2.0 Ghz (Python)
340 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.
341 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.
342 weakm3d 5.31 % 8.19 % 3.83 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
343 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.
344 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.
345 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.
346 UDI-mono3D 3.08 % 3.93 % 2.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
347 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.
348 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 .
349 GAA 0.00 % 0.00 % 0.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
350 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.
351 GA-Aug 0.00 % 0.00 % 0.00 % 0.04 s GPU @ 2.5 Ghz (Python)
352 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 ADLAB 52.58 % 58.39 % 49.13 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
2 VPFNet code 52.41 % 60.07 % 50.28 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
3 SIEV-Net 52.15 % 60.78 % 48.54 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
4 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.
5 ISE-RCNN 51.06 % 55.64 % 47.76 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
6 HIKVISION-AFree 50.67 % 56.54 % 48.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 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.
8 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.
9 H^23D R-CNN 50.43 % 58.14 % 46.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
10 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.
11 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.
12 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.
13 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.
14 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.
15 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.
16 HIKVISION-ADLab-HZ 49.62 % 55.94 % 47.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 SAA-PV-RCNN 49.58 % 57.07 % 46.49 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
18 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.
19 AutoAlign 49.27 % 59.28 % 46.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
20 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.
21 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.
22 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.
23 EA-M-RCNN(BorderAtt) 48.68 % 57.06 % 45.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
24 VCT 48.67 % 54.64 % 46.62 % 0.2 s 1 core @ 2.5 Ghz (Python)
25 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.
26 EPNet++ 48.47 % 56.24 % 45.73 % 0.1 s GPU @ 2.5 Ghz (Python)
27 MGAF-3DSSD code 48.46 % 56.09 % 44.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
28 Fast 48.27 % 57.19 % 44.55 % 0.1 s GPU @ 2.5 Ghz (Python)
29 FromVoxelToPoint code 48.15 % 56.54 % 45.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
30 TBD 47.95 % 53.09 % 45.90 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
31 ISE-RCNN-PV 47.85 % 55.63 % 45.80 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
32 P2V-RCNN 47.36 % 54.15 % 45.10 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
33 SGNet 47.29 % 53.84 % 44.10 % 0.09 s GPU @ 2.5 Ghz (Python)
34 VFN 47.12 % 56.71 % 44.42 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
35 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.
36 SCIR-Net
This method makes use of Velodyne laser scans.
46.76 % 53.47 % 43.72 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
37 PPAF
This method makes use of Velodyne laser scans.
46.74 % 53.44 % 43.76 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
38 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.
39 TBD_IOU1 46.59 % 53.92 % 44.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 XView-PartA^2 46.57 % 52.45 % 43.85 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
41 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.
42 GNN-RCNN 46.10 % 52.19 % 43.97 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
43 TBD_IOU 46.08 % 53.25 % 43.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
44 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.
45 E^2-PV-RCNN 45.85 % 52.35 % 44.00 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
46 DSA-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, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
47 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.
48 SAA-SECOND 45.47 % 53.95 % 42.77 % 38m s 1 core @ 2.5 Ghz (C/C++)
49 FusionDetv2-v3 45.41 % 51.16 % 42.72 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
50 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.
51 Generalized-SIENet 45.39 % 51.66 % 43.51 % 0.08 s 1 core @ 2.5 Ghz (Python)
52 SA-voxel-centernet code 45.35 % 51.16 % 43.33 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
53 WHUT-iou_ssd code 45.24 % 50.30 % 43.28 % 0.045s 1 core @ 2.5 Ghz (C/C++)
54 sa-voxel-centernet code 45.20 % 51.01 % 43.25 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
55 FPCR-CNN 45.18 % 52.79 % 42.70 % 0.05 s 1 core @ 2.5 Ghz (Python)
56 TPCG 45.17 % 51.44 % 43.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 IA-SSD (single) 45.07 % 52.73 % 42.75 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
58 Point Image Fusion 45.07 % 50.56 % 42.92 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
59 PF-GAP 45.02 % 53.73 % 41.88 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
60 GAP-soft-filter 44.98 % 52.44 % 42.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
61 FPC-RCNN 44.96 % 51.54 % 42.88 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
62 FusionDetv1 44.85 % 52.42 % 42.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
63 Fast VP-RCNN code 44.84 % 51.19 % 42.63 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
64 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
44.84 % 52.42 % 42.56 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
65 FusionDetv2-v2 44.80 % 50.61 % 42.91 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
66 TBD 44.65 % 50.72 % 42.61 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
67 FusionDetv2-v5 44.64 % 51.44 % 42.32 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
68 SVGA-Net
This method makes use of Velodyne laser scans.
44.57 % 51.45 % 42.45 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
69 anonymous code 44.50 % 50.60 % 42.26 % 0.05s 1 core @ >3.5 Ghz (python)
70 FSA-PVRCNN
This method makes use of Velodyne laser scans.
44.49 % 49.33 % 42.58 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
71 FusionDetv2-v4 44.47 % 50.88 % 42.18 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
72 P2V_PCV1 44.33 % 49.29 % 41.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 MVOD 44.32 % 50.38 % 42.37 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
74 SIF 44.28 % 52.05 % 42.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
75 ST-RCNN
This method makes use of Velodyne laser scans.
44.14 % 49.78 % 41.95 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
76 DVFENet 44.12 % 50.98 % 41.62 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
77 SARFE 44.00 % 49.85 % 41.99 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
78 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 43.85 % 52.15 % 41.68 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
79 TBD 43.69 % 52.35 % 41.24 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
80 S-AT GCN 43.43 % 50.63 % 41.58 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
81 FPC3D_all
This method makes use of Velodyne laser scans.
43.41 % 50.05 % 41.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
82 tbd 43.41 % 49.45 % 40.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 MKFFNet 43.29 % 50.71 % 40.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 demo 43.29 % 51.78 % 40.79 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
85 BirdNet+
This method makes use of Velodyne laser scans.
code 42.87 % 48.90 % 40.59 % 0.11 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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
86 MSL3D 42.82 % 48.81 % 40.13 % 0.03 s GPU @ 2.5 Ghz (Python)
87 IA-SSD (multi) 42.61 % 51.76 % 40.51 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
88 MKFFNet 42.58 % 49.79 % 40.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
89 FusionDetv2-baseline 42.53 % 47.08 % 40.71 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
90 YF 42.43 % 50.18 % 39.99 % 0.04 s GPU @ 2.5 Ghz (C/C++)
91 XView 42.42 % 47.24 % 39.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
92 VGCN 42.33 % 50.02 % 40.05 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
93 IGRP+ 41.86 % 50.15 % 38.98 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
94 TBD 41.70 % 49.81 % 39.43 % TBD GPU @ 2.5 Ghz (Python + C/C++)
95 AF_MCLS 41.61 % 50.55 % 37.85 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
96 ASCNet 41.46 % 47.25 % 38.83 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
97 MKFFNet 41.33 % 47.80 % 39.39 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
98 PFF3D
This method makes use of Velodyne laser scans.
code 40.94 % 48.74 % 38.54 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
99 NV2P-RCNN 40.71 % 46.83 % 38.86 % 0.1 s GPU @ 2.5 Ghz (Python)
100 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.
101 BirdNet+ (legacy)
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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
102 HS3D code 38.11 % 44.05 % 34.73 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
103 RoIFusion code 38.08 % 46.21 % 35.97 % 0.22 s 1 core @ 3.0 Ghz (Python)
104 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.
105 CBi-GNN-persons 36.56 % 45.80 % 32.68 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
106 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.
107 MMLAB LIGA-Stereo
This method uses stereo information.
code 34.13 % 44.71 % 30.42 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
108 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.
109 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.
110 FusionDetv2-v1 32.24 % 37.46 % 31.61 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
111 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.
112 deleted 29.50 % 40.58 % 26.01 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
113 PointRGBNet 29.32 % 38.07 % 26.94 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
114 Disp R-CNN
This method uses stereo information.
code 29.12 % 42.72 % 25.09 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
115 LIGA-Stereo-old
This method uses stereo information.
28.84 % 36.99 % 25.78 % 0.375 s Titan Xp
116 Disp R-CNN (velo)
This method uses stereo information.
code 28.34 % 40.21 % 24.46 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
117 NCL code 27.06 % 31.78 % 25.63 % NA s 1 core @ 2.5 Ghz (Python)
118 OSE+ 26.02 % 36.60 % 22.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
119 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.
120 AEC3D 22.40 % 28.59 % 20.67 % 18 ms GPU @ 2.5 Ghz (Python)
121 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.
122 YOLOStereo3D
This method uses stereo information.
code 20.76 % 31.01 % 18.41 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
123 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.
124 BEVC 20.50 % 26.84 % 18.71 % 35ms GPU @ 1.5 Ghz (Python)
125 VN3D 19.12 % 23.51 % 16.26 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
126 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.
127 SOD 15.49 % 23.56 % 13.38 % 0.1 s 1 core @ 2.5 Ghz (Python)
128 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.
129 RT3D-GMP
This method uses stereo information.
14.22 % 19.92 % 12.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
130 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.
131 DD3D code 10.85 % 15.90 % 9.41 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
132 SwinMono3D 9.82 % 14.55 % 8.31 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
133 SCSTSV-MonoFlex 9.62 % 14.45 % 8.14 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
134 CaDDN code 9.41 % 14.72 % 8.17 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
135 MM 9.39 % 15.39 % 8.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
136 K3D 9.06 % 14.56 % 7.59 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
137 MonoFlex 8.91 % 13.26 % 7.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
138 MonoLCD 8.89 % 12.73 % 7.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
139 vadin-TBD 8.81 % 13.26 % 7.41 % 0.04 s 1 core @ 2.5 Ghz (Python)
140 MonoCon 8.73 % 13.55 % 7.83 % 0.02 s GPU @ 2.5 Ghz (Python)
141 mono3d 8.30 % 12.67 % 6.94 % 0.03 s GPU @ 2.5 Ghz (Python)
142 LPCG-Monoflex 7.92 % 12.11 % 6.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
143 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.
144 MonoRUn code 7.59 % 11.70 % 6.34 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
145 MonoFlex 7.36 % 10.36 % 6.29 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
146 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.
147 monodle code 6.96 % 10.73 % 6.20 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
148 GAC3D++ 6.92 % 10.56 % 5.70 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
149 MonoGeo 6.77 % 9.54 % 5.83 % 0.05 s 1 core @ 2.5 Ghz (Python)
150 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.
151 RelationNet3D_dla34 code 6.54 % 10.17 % 5.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
152 ICCV 6.29 % 9.28 % 5.29 % 0.04 s GPU @ 2.5 Ghz (Python)
153 DA-Mono3D 6.22 % 8.96 % 5.17 % 0.09s 1 core @ 2.5 Ghz (C/C++)
154 M3DSSD++ 6.19 % 9.24 % 5.54 % 0.16s 1 core @ 2.5 Ghz (C/C++)
155 E2E-DA 6.15 % 10.33 % 5.99 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
156 Deprecated 6.12 % 8.70 % 5.16 % Deprecated Deprecated
157 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.
158 MonoHMOO 5.62 % 8.69 % 5.25 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
159 RelationNet3D_res18 code 5.50 % 8.86 % 5.04 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
160 Aug3D-RPN 5.22 % 7.14 % 4.21 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
161 PLDet3d 4.91 % 7.18 % 3.93 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
162 Lite-FPN 4.79 % 7.13 % 4.26 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
163 COF3D 4.78 % 7.20 % 4.55 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
164 MAOLoss code 4.74 % 6.63 % 4.19 % 0.05 s 1 core @ 2.5 Ghz (Python)
165 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.
166 E2E-DA-Lite (Res18) 4.59 % 5.98 % 3.53 % 0.01 s GPU @ 2.5 Ghz (Python)
167 MP-Mono 4.59 % 6.04 % 3.96 % 0.16 s GPU @ 2.5 Ghz (Python)
168 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.
169 DFR-Net 4.52 % 6.66 % 3.71 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
170 M3D-RPN(S-R) 4.46 % 6.53 % 4.10 % 0.16 s GPU @ 1.5 Ghz (Python)
171 FADNet code 4.45 % 6.46 % 3.70 % 0.04 s GPU @ >3.5 Ghz (Python)
172 QD-3DT
This is an online method (no batch processing).
code 4.23 % 6.62 % 3.39 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
173 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 .
174 DDMP-3D 4.02 % 5.53 % 3.36 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
175 Geo3D 3.95 % 6.18 % 3.66 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
176 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.
177 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.
178 LAPNet 3.59 % 4.86 % 2.98 % 0.03 s 1 core @ 2.5 Ghz (Python)
179 TBD 3.26 % 4.19 % 2.72 % TBD TBD
180 CD-KD 3.26 % 4.75 % 2.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
181 MonoEF code 3.05 % 4.61 % 2.85 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
182 TBD 2.58 % 3.89 % 2.25 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
183 PPTrans 2.58 % 4.01 % 2.37 % 0.2 s GPU @ 2.5 Ghz (Python)
184 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.
185 SparVox3D 2.05 % 2.90 % 1.69 % 0.05 s GPU @ 2.0 Ghz (Python)
186 UDI-mono3D 1.85 % 2.94 % 1.60 % 0.05 s 1 core @ 2.5 Ghz (Python)
187 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 ISE-RCNN-PV 75.40 % 86.08 % 68.58 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
2 ISE-RCNN 74.49 % 85.93 % 67.96 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
3 HIKVISION-AFree 74.08 % 87.14 % 66.16 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 SGNet 73.88 % 88.03 % 66.84 % 0.09 s GPU @ 2.5 Ghz (Python)
5 Point Image Fusion 73.51 % 85.81 % 66.29 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
6 DSA-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, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
7 anonymous code 72.55 % 85.63 % 65.33 % 0.05s 1 core @ >3.5 Ghz (python)
8 PPAF
This method makes use of Velodyne laser scans.
72.24 % 83.58 % 64.65 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
9 SAA-PV-RCNN 72.24 % 84.12 % 64.70 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
10 Fast VP-RCNN code 72.07 % 84.39 % 65.02 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
11 sa-voxel-centernet code 71.90 % 82.76 % 65.41 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
12 GNN-RCNN 71.90 % 85.09 % 65.27 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
13 E^2-PV-RCNN 71.89 % 84.41 % 65.15 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
14 PV-RCNN-v2 71.86 % 84.60 % 63.84 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
15 TPCG 71.81 % 84.74 % 64.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
16 BtcDet
This method makes use of Velodyne laser scans.
71.76 % 84.48 % 64.70 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
17 HIKVISION-ADLab-HZ 71.75 % 85.66 % 65.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
18 SA-voxel-centernet code 71.70 % 82.46 % 64.98 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
19 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.
20 RangeIoUDet
This method makes use of Velodyne laser scans.
71.49 % 85.99 % 63.62 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
21 IA-SSD (single) 71.44 % 85.91 % 63.41 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
22 SARFE 71.36 % 84.49 % 63.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
23 Generalized-SIENet 71.21 % 84.64 % 64.61 % 0.08 s 1 core @ 2.5 Ghz (Python)
24 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.
25 FPC-RCNN 70.93 % 83.75 % 63.47 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
26 AutoAlign 70.55 % 85.98 % 63.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
27 WHUT-iou_ssd code 70.53 % 82.35 % 63.19 % 0.045s 1 core @ 2.5 Ghz (C/C++)
28 TBD 70.48 % 87.26 % 63.98 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
29 SPG_mini
This method makes use of Velodyne laser scans.
70.09 % 82.66 % 63.61 % 0.09 s GPU @ 2.5 Ghz (Python)
30 VCT 69.96 % 85.63 % 63.59 % 0.2 s 1 core @ 2.5 Ghz (Python)
31 FSA-PVRCNN
This method makes use of Velodyne laser scans.
69.67 % 81.86 % 63.32 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
32 ASCNet 69.48 % 81.01 % 62.42 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
33 XView-PartA^2 69.43 % 83.48 % 63.18 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
34 ST-RCNN
This method makes use of Velodyne laser scans.
69.42 % 80.69 % 62.63 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
35 TBD 69.41 % 82.71 % 61.77 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
36 MVOD 69.37 % 82.85 % 61.93 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
37 CBi-GNN-persons 69.23 % 82.37 % 61.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
38 TBD 69.08 % 83.68 % 62.28 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
39 JPVNet 69.07 % 83.46 % 62.73 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
40 EA-M-RCNN(BorderAtt) 69.06 % 83.54 % 61.13 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
41 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.
42 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.
43 FusionDetv2-v5 68.84 % 80.42 % 61.90 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
44 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.
45 MSL3D 68.57 % 81.23 % 62.01 % 0.03 s GPU @ 2.5 Ghz (Python)
46 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.
47 NV2P-RCNN 68.31 % 78.63 % 61.08 % 0.1 s GPU @ 2.5 Ghz (Python)
48 P2V-RCNN 68.06 % 81.09 % 60.73 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
49 H^23D R-CNN code 67.90 % 82.76 % 60.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
50 RoIFusion code 67.71 % 83.13 % 61.70 % 0.22 s 1 core @ 3.0 Ghz (Python)
51 VPFNet code 67.66 % 80.83 % 61.36 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
52 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.
53 Fast 67.55 % 83.34 % 59.61 % 0.1 s GPU @ 2.5 Ghz (Python)
54 DVFENet 67.40 % 82.29 % 60.71 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
55 FromVoxelToPoint code 67.36 % 82.68 % 59.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
56 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.
57 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.
58 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.
59 tbd 67.20 % 81.16 % 59.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
60 SCIR-Net
This method makes use of Velodyne laser scans.
67.17 % 80.95 % 60.55 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
61 FPCR-CNN 67.17 % 82.51 % 60.33 % 0.05 s 1 core @ 2.5 Ghz (Python)
62 MKFFNet 67.10 % 80.98 % 60.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 TBD_IOU 67.09 % 82.97 % 59.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 VGCN 67.04 % 81.50 % 59.45 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
65 TBD_IOU1 66.95 % 81.77 % 58.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 S-AT GCN 66.71 % 78.53 % 60.19 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
67 SAA-SECOND 66.71 % 81.56 % 59.60 % 38m s 1 core @ 2.5 Ghz (C/C++)
68 SVGA-Net
This method makes use of Velodyne laser scans.
66.66 % 78.93 % 59.55 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
69 FusionDetv2-v3 66.60 % 80.54 % 58.90 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
70 FusionDetv2-v4 66.54 % 82.50 % 59.78 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
71 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.
72 IA-SSD (multi) 66.29 % 81.30 % 59.58 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
73 MKFFNet 66.16 % 81.36 % 58.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 MKFFNet 66.09 % 78.95 % 59.58 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
75 FusionDetv2-v2 66.01 % 80.29 % 59.63 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
76 MGAF-3DSSD code 66.00 % 83.03 % 57.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
77 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.
78 P2V_PCV1 65.34 % 78.44 % 58.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 TBD 64.87 % 79.46 % 58.67 % TBD GPU @ 2.5 Ghz (Python + C/C++)
80 FPC3D_all
This method makes use of Velodyne laser scans.
64.66 % 78.81 % 58.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
81 CCFNET 64.65 % 81.29 % 57.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
82 demo 64.55 % 78.63 % 58.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
83 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 64.54 % 79.65 % 57.84 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
84 FusionDetv1 64.53 % 79.62 % 57.91 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
85 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
64.52 % 79.64 % 57.90 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
86 VFN 64.34 % 81.14 % 57.88 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
87 AF_MCLS 64.34 % 82.45 % 57.39 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
88 SIF 64.13 % 79.32 % 57.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
89 GAP-soft-filter 64.02 % 79.39 % 57.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
90 FusionDetv2-baseline 63.77 % 76.64 % 57.01 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
91 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.
92 YF 63.54 % 75.92 % 57.59 % 0.04 s GPU @ 2.5 Ghz (C/C++)
93 SIEV-Net 63.21 % 82.22 % 56.41 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
94 XView 63.06 % 81.32 % 56.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
95 EPNet++ 62.94 % 78.57 % 56.62 % 0.1 s GPU @ 2.5 Ghz (Python)
96 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.
97 PF-GAP 62.49 % 78.64 % 55.87 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
98 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.
99 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.
100 BirdNet+
This method makes use of Velodyne laser scans.
code 59.58 % 70.84 % 54.20 % 0.11 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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
101 IGRP+ 57.94 % 76.25 % 51.86 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
102 PointRGBNet 57.59 % 73.09 % 51.78 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
103 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.
104 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.
105 PFF3D
This method makes use of Velodyne laser scans.
code 55.71 % 72.67 % 49.58 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
106 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.
107 BirdNet+ (legacy)
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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
108 HS3D code 51.36 % 66.14 % 46.12 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
109 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.
110 FusionDetv2-v1 47.75 % 60.34 % 43.53 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
111 LIGA-Stereo-old
This method uses stereo information.
42.42 % 60.23 % 37.03 % 0.375 s Titan Xp
112 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.
113 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.
114 MMLAB LIGA-Stereo
This method uses stereo information.
code 40.60 % 58.95 % 35.27 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
115 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.
116 deleted 36.64 % 53.72 % 32.22 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
117 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.
118 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.
119 SOD 28.81 % 44.90 % 24.37 % 0.1 s 1 core @ 2.5 Ghz (Python)
120 Disp R-CNN (velo)
This method uses stereo information.
code 27.04 % 44.19 % 23.58 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
121 Disp R-CNN
This method uses stereo information.
code 27.04 % 44.19 % 23.58 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
122 AEC3D 26.17 % 36.57 % 25.21 % 18 ms GPU @ 2.5 Ghz (Python)
123 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.
124 VN3D 23.77 % 31.62 % 21.74 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
125 OSE+ 23.55 % 38.05 % 20.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
126 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.
127 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.
128 BEVC 16.74 % 25.98 % 16.02 % 35ms GPU @ 1.5 Ghz (Python)
129 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.
130 RT3D-GMP
This method uses stereo information.
13.92 % 20.59 % 12.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
131 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.
132 CD-KD 6.58 % 11.70 % 5.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
133 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.
134 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.
135 RelationNet3D_dla34 code 5.40 % 9.63 % 4.60 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
136 CaDDN code 5.38 % 9.67 % 4.75 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
137 TBD 5.33 % 9.58 % 4.62 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
138 E2E-DA 4.99 % 8.31 % 4.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
139 LPCG-Monoflex 4.90 % 8.14 % 3.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
140 SCSTSV-MonoFlex 4.50 % 7.40 % 3.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
141 MAOLoss code 4.49 % 7.28 % 3.60 % 0.05 s 1 core @ 2.5 Ghz (Python)
142 E2E-DA-Lite (Res18) 4.31 % 8.03 % 3.20 % 0.01 s GPU @ 2.5 Ghz (Python)
143 DA-Mono3D 4.18 % 7.05 % 4.31 % 0.09s 1 core @ 2.5 Ghz (C/C++)
144 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.
145 vadin-TBD 4.09 % 6.81 % 3.78 % 0.04 s 1 core @ 2.5 Ghz (Python)
146 DFR-Net 4.00 % 5.99 % 3.95 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
147 MonoGeo 3.87 % 5.93 % 3.42 % 0.05 s 1 core @ 2.5 Ghz (Python)
148 MM 3.63 % 7.05 % 3.33 % 1 s 1 core @ 2.5 Ghz (C/C++)
149 K3D 3.39 % 6.16 % 3.13 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
150 MonoLCD 3.33 % 5.27 % 2.90 % 0.04 s 1 core @ 2.5 Ghz (Python)
151 Aug3D-RPN 3.33 % 5.44 % 2.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
152 ICCV 3.32 % 6.59 % 3.13 % 0.04 s GPU @ 2.5 Ghz (Python)
153 monodle code 3.28 % 5.34 % 2.83 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
154 mono3d 3.24 % 5.63 % 2.74 % 0.03 s GPU @ 2.5 Ghz (Python)
155 DDMP-3D 3.14 % 4.92 % 2.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
156 M3DSSD++ 3.14 % 5.73 % 3.03 % 0.16s 1 core @ 2.5 Ghz (C/C++)
157 QD-3DT
This is an online method (no batch processing).
code 3.02 % 5.71 % 2.73 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
158 RelationNet3D_res18 code 2.91 % 5.49 % 2.48 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
159 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.
160 Deprecated 2.80 % 3.96 % 2.32 % Deprecated Deprecated
161 SwinMono3D 2.78 % 4.50 % 2.53 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
162 MonoCon 2.68 % 3.87 % 2.24 % 0.02 s GPU @ 2.5 Ghz (Python)
163 MonoFlex 2.67 % 4.41 % 2.50 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
164 GAC3D++ 2.53 % 4.69 % 2.48 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
165 MonoFlex 2.51 % 4.36 % 2.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
166 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.
167 TBD 2.34 % 3.46 % 2.02 % TBD TBD
168 Geo3D 2.21 % 4.16 % 2.18 % 0.04 s GPU @ 2.5 Ghz (Python)
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169 UDI-mono3D 2.16 % 3.81 % 1.65 % 0.05 s 1 core @ 2.5 Ghz (Python)
170 PPTrans 2.07 % 3.44 % 1.77 % 0.2 s GPU @ 2.5 Ghz (Python)
171 DD3D code 1.99 % 3.20 % 1.79 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
172 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.
173 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.
174 MP-Mono 1.74 % 2.78 % 1.86 % 0.16 s GPU @ 2.5 Ghz (Python)
175 MonoHMOO 1.65 % 1.91 % 1.75 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
176 COF3D 1.60 % 2.70 % 1.55 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
177 PLDet3d 1.45 % 2.21 % 1.58 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
178 MonoEF code 1.18 % 2.36 % 1.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
179 LAPNet 1.03 % 1.71 % 1.04 % 0.03 s 1 core @ 2.5 Ghz (Python)
180 FADNet code 0.94 % 1.54 % 0.79 % 0.04 s GPU @ >3.5 Ghz (Python)
181 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 .
182 MonoRUn code 0.73 % 1.14 % 0.66 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
183 Lite-FPN 0.44 % 0.52 % 0.27 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
184 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.
185 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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