3D Object Detection Evaluation 2017


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

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

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 VirConv-S code 87.20 % 92.48 % 82.45 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
2 UDeerPEP code 86.72 % 91.77 % 82.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Z. Dong, H. Ji, X. Huang, W. Zhang, X. Zhan and J. Chen: PeP: a Point enhanced Painting method for unified point cloud tasks. 2023.
3 VirConv-T code 86.25 % 92.54 % 81.24 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
4 ViKIENet-R 86.04 % 91.20 % 81.18 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
5 BFT3D 85.65 % 92.32 % 78.88 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
6 MPCF code 85.50 % 92.46 % 80.69 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
P. Gao and P. Zhang: MPCF: Multi-Phase Consolidated Fusion for Multi-Modal 3D Object Detection with Pseudo Point Cloud. 2024.
7 TSSTDet 85.47 % 91.84 % 80.65 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang, D. Bui and M. Yoo: TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer. IEEE Sensors Journal 2024.
8 3ONet 85.47 % 92.03 % 78.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
9 mm3d 85.45 % 92.09 % 80.68 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
10 mat3D 85.36 % 92.01 % 80.58 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
11 TED code 85.28 % 91.61 % 80.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
12 MB3D 85.24 % 91.43 % 80.28 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
13 PVFusion code 85.07 % 90.98 % 80.16 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
14 LoGoNet code 85.06 % 91.80 % 80.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
15 TRTConv-L 85.04 % 91.90 % 80.38 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
16 ViKIENet 84.96 % 91.79 % 80.20 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
17 ANM code 84.92 % 91.46 % 81.87 % ANM ANM
18 LVP 84.92 % 91.37 % 80.07 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
19 BVPConv-T 84.83 % 91.59 % 80.38 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
20 MM-UniMODE 84.81 % 91.23 % 81.44 % 0.04 s 1 core @ 2.5 Ghz (Python)
21 TRTConv-T 84.80 % 91.74 % 80.22 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
22 SFD code 84.76 % 91.73 % 77.92 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
23 RM3D 84.72 % 90.86 % 81.17 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
24 ACFNet 84.67 % 90.80 % 80.14 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
25 SCEMF 84.66 % 91.19 % 81.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
26 OGMMDet code 84.51 % 91.82 % 79.80 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
27 BVPConv-L 84.40 % 91.38 % 80.07 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
28 SSLFusion 84.38 % 91.43 % 80.04 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
29 MuStD 84.36 % 91.03 % 80.78 % 67 ms >8 cores @ 2.5 Ghz (Python)
30 3D HANet code 84.18 % 90.79 % 77.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, Y. Chen, G. Cai, G. Chen, D. Xie, J. Su and Z. Wang: 3D HANet: A Flexible 3D Heatmap Auxiliary Network for Object Detection. IEEE Transactions on Geoscience and Remote Sensing 2023.
31 CasA++ code 84.04 % 90.68 % 79.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
32 TED_S_baseline code 83.99 % 90.75 % 79.63 % 0.09 s 1 core @ 2.5 Ghz (Python)
33 L-AUG 83.84 % 90.53 % 79.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
34 MLFusion-VS 83.71 % 91.12 % 79.74 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
35 HS-fusion 83.42 % 89.12 % 78.60 % - s 1 core @ 2.5 Ghz (Python)
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36 SFA-GCL code 83.32 % 92.12 % 78.07 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
37 LumiNet code 83.32 % 91.76 % 78.29 % 0.1 s 1 core @ 2.5 Ghz (Python)
38 LFT 83.32 % 91.80 % 78.29 % 0.1s 1 core @ 2.5 Ghz (C/C++)
39 SFA-GCL(80) code 83.29 % 91.96 % 78.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
40 GraR-VoI code 83.27 % 91.89 % 77.78 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
41 GLENet-VR code 83.23 % 91.67 % 78.43 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D object detectors with generative label uncertainty estimation. International Journal of Computer Vision 2023.
Y. Zhang, J. Hou and Y. Yuan: A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks. International Journal of Computer Vision 2023.
42 VPFNet code 83.21 % 91.02 % 78.20 % 0.06 s 2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. IEEE Transactions on Multimedia 2022.
43 GraR-Po code 83.18 % 91.79 % 77.98 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
44 CasA code 83.06 % 91.58 % 80.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
45 SFA-GCL(80, k=4) code 83.05 % 91.91 % 77.84 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
46 UPIDet code 82.97 % 89.13 % 80.05 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
47 Anonymous code 82.93 % 91.31 % 78.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
48 ECA 82.90 % 88.58 % 78.57 % 0.08 s GPU @ 1.5 Ghz (Python)
49 MLF-DET 82.89 % 91.18 % 77.89 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
50 BtcDet
This method makes use of Velodyne laser scans.
 code 82.86 % 90.64 % 78.09 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
51 R2Pfusion-Det 82.83 % 89.20 % 80.02 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
52 VPA 82.78 % 91.62 % 77.97 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
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53 GraR-Vo code 82.77 % 91.29 % 77.20 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
54 SPG_mini
This method makes use of Velodyne laser scans.
 code 82.66 % 90.64 % 77.91 % 0.09 s GPU @ 2.5 Ghz (Python)
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.
55 OcTr 82.64 % 90.88 % 77.77 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
C. Zhou, Y. Zhang, J. Chen and D. Huang: OcTr: Octree-based Transformer for 3D Object Detection. CVPR 2023.
56 DiffCandiDet 82.59 % 91.18 % 77.64 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
57 PA3DNet 82.57 % 90.49 % 77.88 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
M. Wang, L. Zhao and Y. Yue: PA3DNet: 3-D Vehicle Detection with Pseudo Shape Segmentation and Adaptive Camera- LiDAR Fusion. IEEE Transactions on Industrial Informatics 2023.
58 SE-SSD
This method makes use of Velodyne laser scans.
 code 82.54 % 91.49 % 77.15 % 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.
59 MPC3DNet 82.52 % 92.19 % 77.55 % 0.05 s GPU @ 1.5 Ghz (Python)
60 DVF-V 82.45 % 89.40 % 77.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
61 GraR-Pi code 82.42 % 90.94 % 77.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
62 SFA-GCL(baseline) code 82.40 % 91.57 % 75.45 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
63 DVF-PV 82.40 % 90.99 % 77.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
64 3D Dual-Fusion code 82.40 % 91.01 % 79.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
Y. Kim, K. Park, M. Kim, D. Kum and J. Choi: 3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection. arXiv preprint arXiv:2211.13529 2022.
65 SFA-GCL code 82.38 % 91.56 % 75.41 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
66 SCNet3D 82.35 % 89.16 % 77.72 % 0.08 s 1 core @ 2.5 Ghz (Python)
67 RDIoU code 82.30 % 90.65 % 77.26 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single- stage 3D Object Detection. ECCV 2022.
68 PVT-SSD 82.29 % 90.65 % 76.85 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He and W. Ouyang: PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer. CVPR 2023.
69 Focals Conv code 82.28 % 90.55 % 77.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
70 SFA-GCL_dataaug code 82.28 % 89.57 % 75.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
71 CLOCs code 82.28 % 89.16 % 77.23 % 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.
72 GraphAlign(ICCV2023) code 82.23 % 90.90 % 79.67 % 0.03 s GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
73 DEF-Model 82.19 % 88.49 % 77.40 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
74 spark 82.18 % 90.66 % 77.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
75 DGEnhCL code 82.18 % 91.12 % 75.29 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
76 SASA
This method makes use of Velodyne laser scans.
 code 82.16 % 88.76 % 77.16 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
77 spark_voxel_rcnn code 82.15 % 90.62 % 77.40 % 0.04 s 1 core @ 2.5 Ghz (Python)
78 PG-RCNN code 82.13 % 89.38 % 77.33 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
79 focal 82.13 % 90.60 % 79.51 % 100 s 1 core @ 2.5 Ghz (Python)
80 GEFPN 82.13 % 90.60 % 79.51 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
81 GeVo 82.13 % 90.60 % 79.51 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
82 SPG
This method makes use of Velodyne laser scans.
 code 82.13 % 90.50 % 78.90 % 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.
83 SDGUFusion 82.12 % 91.03 % 77.67 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
84 voxel_spark code 82.10 % 90.47 % 79.01 % 0.04 s GPU @ 2.5 Ghz (C/C++)
85 VoTr-TSD code 82.09 % 89.90 % 79.14 % 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.
86 Voxel_Spark_focal_we code 82.08 % 90.65 % 77.36 % 0.08 s 1 core @ 2.5 Ghz (Python)
87 Pyramid R-CNN 82.08 % 88.39 % 77.49 % 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.
88 VoxSeT code 82.06 % 88.53 % 77.46 % 33 ms 1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
89 c2f 82.05 % 89.69 % 79.05 % 1 s 1 core @ 2.5 Ghz (C/C++)
90 DDF 82.03 % 89.69 % 79.47 % 0.1 s 1 core @ 2.5 Ghz (Python)
91 LCANet 82.03 % 88.35 % 77.33 % 1 s 1 core @ 2.5 Ghz (C/C++)
92 BFT3D_easy 82.03 % 92.75 % 74.92 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
93 LGNet-Car code 82.02 % 90.65 % 77.34 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
94 EQ-PVRCNN code 82.01 % 90.13 % 77.53 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
95 BPG3D 81.98 % 90.52 % 78.97 % 0.05 s 1 core @ 2.5 Ghz (Python)
96 voxel-rcnn+++ code 81.97 % 90.59 % 77.13 % 0.08 s GPU @ 2.5 Ghz (Python)
97 EPNet++ 81.96 % 91.37 % 76.71 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
98 USVLab BSAODet code 81.95 % 88.66 % 77.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
99 Spark_partA22 81.94 % 90.24 % 76.95 % 10 s 1 core @ 2.5 Ghz (Python)
100 HMFI code 81.93 % 88.90 % 77.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
101 focalnet 81.92 % 90.59 % 79.25 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
102 AFFN-G 81.92 % 90.57 % 79.24 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
103 focalnet 81.92 % 90.57 % 79.24 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
104 RagNet3D code 81.91 % 88.74 % 77.45 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Chen, Y. Han, Z. Yan, J. Qian, J. Li and J. Yang: Ragnet3d: Learning Distinguishable Representation for Pooled Grids in 3d Object Detection. Available at SSRN 4979473 .
105 spark2 81.88 % 88.61 % 77.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
106 PDV code 81.86 % 90.43 % 77.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
107 SQD code 81.82 % 91.58 % 79.07 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Mo, Y. Wu, J. Zhao, Z. Hou, W. Huang, Y. Hu, J. Wang and J. Yan: Sparse Query Dense: Enhancing 3D Object Detection with Pseudo Points. ACM MM Oral 2024.
108 Spark_PartA2_Soft_fo code 81.82 % 90.10 % 78.35 % 0.1 s 1 core @ 2.5 Ghz (Python)
109 CityBrainLab-CT3D code 81.77 % 87.83 % 77.16 % 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.
110 M3DeTR code 81.73 % 90.28 % 76.96 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
111 HA-PillarNet 81.72 % 90.86 % 77.32 % 0.05 s 1 core @ 2.5 Ghz (Python)
112 SIENet code 81.71 % 88.22 % 77.22 % 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.
113 FIRM-Net 81.65 % 88.25 % 76.98 % 0.07 s 1 core @ 2.5 Ghz (Python)
114 Voxel R-CNN code 81.62 % 90.90 % 77.06 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
115 BADet code 81.61 % 89.28 % 76.58 % 0.14 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.
116 FromVoxelToPoint code 81.58 % 88.53 % 77.37 % 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.
117 test 81.58 % 90.04 % 76.53 % 0.04 s GPU @ 1.5 Ghz (Python + C/C++)
118 LGNet-3classes code 81.57 % 90.84 % 76.98 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
119 H^23D R-CNN code 81.55 % 90.43 % 77.22 % 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.
120 test 81.55 % 88.47 % 76.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
121 FARP-Net code 81.53 % 88.36 % 78.98 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
T. Xie, L. Wang, K. Wang, R. Li, X. Zhang, H. Zhang, L. Yang, H. Liu and J. Li: FARP-Net: Local-Global Feature Aggregation and Relation-Aware Proposals for 3D Object Detection. IEEE Transactions on Multimedia 2023.
122 VoxelFSD 81.50 % 89.89 % 76.82 % 0.08 s 1 core @ 2.5 Ghz (Python)
123 spark-part2 81.49 % 89.82 % 76.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
124 FocalsConv* 81.48 % 90.48 % 77.18 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
125 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 81.46 % 88.25 % 76.96 % 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.
126 P2V-RCNN 81.45 % 88.34 % 77.20 % 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.
127 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 81.43 % 90.25 % 76.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
128 XView 81.35 % 89.21 % 76.87 % 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.
129 RangeRCNN
This method makes use of Velodyne laser scans.
 81.33 % 88.47 % 77.09 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
130 CAT-Det 81.32 % 89.87 % 76.68 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
131 PV-RCNN-Plus 81.29 % 87.72 % 76.78 % 1 s 1 core @ 2.5 Ghz (C/C++)
132 PASS-PV-RCNN-Plus 81.28 % 87.65 % 76.79 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
133 SP_SECOND_IOU code 81.25 % 89.50 % 76.69 % 0.04 s 1 core @ 2.5 Ghz (Python)
134 CGML 81.22 % 90.32 % 76.97 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
135 AFFN-Ga 81.17 % 88.36 % 76.89 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
136 MFB3D 81.11 % 90.57 % 76.62 % 0.14 s 1 core @ 2.5 Ghz (Python)
137 AFFN 81.06 % 89.65 % 76.67 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
138 VPFNet code 80.97 % 88.51 % 76.74 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
C. Wang, H. Chen, Y. Chen, P. Hsiao and L. Fu: VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2024.
139 CG-SSD 80.97 % 87.87 % 76.54 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
140 Sem-Aug
This method makes use of Velodyne laser scans.
 80.77 % 89.41 % 75.90 % 0.1 s GPU @ 2.5 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.
141 StructuralIF 80.69 % 87.15 % 76.26 % 0.02 s 8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
142 CLOCs_PVCas code 80.67 % 88.94 % 77.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
143 SVGA-Net 80.47 % 87.33 % 75.91 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
144 KPTr 80.40 % 88.52 % 75.28 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
145 SRDL 80.38 % 87.73 % 76.27 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
146 Fast-CLOCs 80.35 % 89.10 % 76.99 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
147 SPANet 80.34 % 91.05 % 74.89 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
148 IA-SSD (single) code 80.32 % 88.87 % 75.10 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
149 GSG-FPS code 80.29 % 88.56 % 75.16 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
150 CIA-SSD
This method makes use of Velodyne laser scans.
 code 80.28 % 89.59 % 72.87 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
151 Test_dif code 80.17 % 88.68 % 75.12 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
152 CAIA_PRO code 80.16 % 88.52 % 75.05 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
153 IA-SSD (multi) code 80.13 % 88.34 % 75.04 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
154 EBM3DOD code 80.12 % 91.05 % 72.78 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
155 3D-CVF at SPA
This method makes use of Velodyne laser scans.
 code 80.05 % 89.20 % 73.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
156 bs 79.95 % 90.52 % 76.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 spark_second code 79.93 % 86.66 % 74.93 % . s 1 core @ 2.5 Ghz (Python)
158 SIF 79.88 % 86.84 % 75.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
159 spark_second_focal_w 79.81 % 86.41 % 75.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
160 RAFDet 79.81 % 88.24 % 75.06 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
161 RangeIoUDet
This method makes use of Velodyne laser scans.
 79.80 % 88.60 % 76.76 % 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.
162 SA-SSD code 79.79 % 88.75 % 74.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
163 STD code 79.71 % 87.95 % 75.09 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
164 MGAF-3DSSD code 79.68 % 88.16 % 72.39 % 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.
165 Struc info fusion II 79.59 % 88.97 % 72.51 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
166 3DSSD code 79.57 % 88.36 % 74.55 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
167 EBM3DOD baseline code 79.52 % 88.80 % 72.30 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
168 Struc info fusion I 79.49 % 88.70 % 74.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
169 PartA2_basline code 79.48 % 88.66 % 76.67 % 0.09 s 1 core @ 2.5 Ghz (Python)
170 Point-GNN
This method makes use of Velodyne laser scans.
 code 79.47 % 88.33 % 72.29 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
171 spark_second2 79.45 % 86.28 % 74.71 % 10 s 1 core @ 2.5 Ghz (Python)
172 sec_spark code 79.44 % 86.08 % 74.70 % 0.03 s GPU @ 2.5 Ghz (Python)
173 DFAF3D 79.37 % 88.59 % 72.21 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
174 SSL-PointGNN code 79.36 % 87.78 % 74.15 % 0.56 s GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
175 PUDet 79.34 % 87.85 % 74.58 % 0.3 s GPU @ 2.5 Ghz (Python)
176 EPNet code 79.28 % 89.81 % 74.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
177 second_iou_baseline code 79.20 % 88.08 % 75.91 % 0.05 s 1 core @ 2.5 Ghz (Python)
178 DVFENet 79.18 % 86.20 % 74.58 % 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.
179 second_iou_baseline 79.05 % 87.81 % 75.81 % 0.03 s 1 core @ 2.5 Ghz (Python)
180 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 79.05 % 87.45 % 76.14 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
181 GD-MAE 79.03 % 88.14 % 73.55 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, T. He, J. Liu, H. Chen, B. Wu, B. Lin, X. He and W. Ouyang: GD-MAE: Generative Decoder for MAE Pre- training on LiDAR Point Clouds. CVPR 2023.
182 3D IoU-Net 79.03 % 87.96 % 72.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
183 SERCNN
This method makes use of Velodyne laser scans.
 78.96 % 87.74 % 74.30 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
184 Second_baseline code 78.94 % 85.85 % 74.28 % 0.03 s 1 core @ 2.5 Ghz (Python)
185 ACDet code 78.85 % 88.47 % 73.86 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
186 MG 78.72 % 87.68 % 72.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
187 MVAF-Net code 78.71 % 87.87 % 75.48 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
188 Res3DNet 78.54 % 87.22 % 74.36 % 0.05 s GPU @ 3.5 Ghz (Python)
189 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 78.49 % 87.81 % 73.51 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
190 CLOCs_SecCas 78.45 % 86.38 % 72.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
191 Patches - EMP
This method makes use of Velodyne laser scans.
 78.41 % 89.84 % 73.15 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
192 HotSpotNet 78.31 % 87.60 % 73.34 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
193 Sem-Aug-PointRCNN++ 78.06 % 86.69 % 73.85 % 0.1 s 8 cores @ 3.0 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.
194 CenterNet3D 77.90 % 86.20 % 73.03 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
195 spark_pointpillar code 77.82 % 87.59 % 73.94 % 0.02 s GPU @ 2.5 Ghz (Python)
196 pointpillars_spark code 77.75 % 87.55 % 73.63 % 0.02 s GPU @ 2.5 Ghz (C/C++)
197 VoxelFSD-S 77.67 % 86.29 % 72.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
198 pointpillar_spark_fo 77.66 % 85.99 % 72.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
199 spark_pointpillar2 77.57 % 85.96 % 72.29 % 10 s 1 core @ 2.5 Ghz (Python)
200 UberATG-MMF
This method makes use of Velodyne laser scans.
 77.43 % 88.40 % 70.22 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
201 Associate-3Ddet code 77.40 % 85.99 % 70.53 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
202 Fast Point R-CNN
This method makes use of Velodyne laser scans.
 77.40 % 85.29 % 70.24 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
203 RangeDet (Official) code 77.36 % 85.41 % 72.60 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
204 Patches
This method makes use of Velodyne laser scans.
 77.20 % 88.67 % 71.82 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
205 EAEPNet 76.89 % 87.62 % 71.90 % 0.1 s 1 core @ 2.5 Ghz (Python)
206 SeSame-point code 76.83 % 85.25 % 71.60 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
207 HRI-VoxelFPN 76.70 % 85.64 % 69.44 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
208 SARPNET 76.64 % 85.63 % 71.31 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
209 3D IoU Loss
This method makes use of Velodyne laser scans.
 76.50 % 86.16 % 71.39 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
210 TF-PartA2 76.39 % 86.65 % 71.67 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
211 F-ConvNet
This method makes use of Velodyne laser scans.
 code 76.39 % 87.36 % 66.69 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
212 pointpillar_baseline code 76.37 % 85.29 % 71.03 % 0.01 s 1 core @ 2.5 Ghz (Python)
213 BAPartA2S-4h 76.31 % 86.97 % 73.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
214 VSAC 76.29 % 85.06 % 71.65 % 0.07 s 1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
215 SegVoxelNet 76.13 % 86.04 % 70.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
216 centerpoint_pcdet 76.12 % 83.47 % 71.17 % 0.06 s 1 core @ 2.5 Ghz (Python)
217 mm3d_PartA2 76.09 % 86.82 % 72.74 % 0.1 s GPU @ >3.5 Ghz (Python)
218 S-AT GCN 76.04 % 83.20 % 71.17 % 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.
219 T-SSD 76.00 % 86.97 % 69.11 % 0.04 1 core @ 2.0 Ghz (C/C++)
220 TANet code 75.94 % 84.39 % 68.82 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
221 PointRGCN 75.73 % 85.97 % 70.60 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
222 R50_SACINet 75.67 % 86.37 % 70.73 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
223 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 75.64 % 86.96 % 70.70 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
224 L_SACINet 75.61 % 84.36 % 70.46 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
225 voxelnext_pcdet 75.58 % 83.88 % 70.77 % 0.05 s 1 core @ 2.5 Ghz (Python)
226 XT-PartA2 75.56 % 85.54 % 71.02 % 0.1 s GPU @ >3.5 Ghz (Python)
227 SecAtten 75.50 % 85.55 % 70.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
228 DensePointPillars 75.46 % 84.60 % 68.43 % 0.03 s GPU @ 2.5 Ghz (Python)
229 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 75.43 % 86.10 % 68.88 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
230 R-GCN 75.26 % 83.42 % 68.73 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
231 PL++: PV-RCNN++
This method uses stereo information.
This method makes use of Velodyne laser scans.
 75.23 % 86.60 % 70.34 % 0.342 s RTX 4060Ti (Python)
232 epBRM
This method makes use of Velodyne laser scans.
 code 75.15 % 85.00 % 69.84 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
233 SeSame-voxel code 75.05 % 81.51 % 70.53 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
234 MAFF-Net(DAF-Pillar) 75.04 % 85.52 % 67.61 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
235 PASS-PointPillar 74.85 % 84.72 % 69.05 % 1 s 1 core @ 2.5 Ghz (C/C++)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
236 PI-RCNN 74.82 % 84.37 % 70.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
237 mmFUSION code 74.38 % 85.24 % 69.43 % 1s 1 core @ 2.5 Ghz (Python)
J. Ahmad and A. Del Bue: mmFUSION: Multimodal Fusion for 3D Objects Detection. arXiv preprint arXiv:2311.04058 2023.
238 PointPillars
This method makes use of Velodyne laser scans.
 code 74.31 % 82.58 % 68.99 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
239 PCNet3D_Extension_V1 74.19 % 84.00 % 69.65 % 0.5 s GPU @ 3.5 Ghz (Python)
240 HINTED code 74.13 % 84.00 % 67.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, W. Ye, H. Wu, S. Zhao, L. Xing, X. Huang, J. Deng, X. Li, C. Wen and C. Wang: HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely- Supervised 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
241 ARPNET 74.04 % 84.69 % 68.64 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
242 Harmonic PointPillar code 73.96 % 82.26 % 69.21 % 0.01 s 1 core @ 2.5 Ghz (Python)
H. Zhang, J. Mekala, Z. Nain, J. Park and H. Jung: 3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection for V2X Orchestration. will submit to IEEE Transactions on Vehicular Technology 2022.
243 SeSame-pillar code 73.85 % 83.88 % 68.65 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
244 PC-CNN-V2
This method makes use of Velodyne laser scans.
 73.79 % 85.57 % 65.65 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
245 C-GCN 73.62 % 83.49 % 67.01 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
246 PCNet3D 73.58 % 83.22 % 68.19 % 0.05 s GPU @ 3.5 Ghz (Python)
247 3DBN
This method makes use of Velodyne laser scans.
 73.53 % 83.77 % 66.23 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
248 PointRGBNet 73.49 % 83.99 % 68.56 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
249 SCNet
This method makes use of Velodyne laser scans.
 73.17 % 83.34 % 67.93 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
250 SeSame-pillar w/scor code 73.15 % 82.32 % 66.64 % N/A s 1 core @ 2.5 Ghz (C/C++)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
251 PFF3D
This method makes use of Velodyne laser scans.
 code 72.93 % 81.11 % 67.24 % 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.
252 MM_SECOND code 72.68 % 82.02 % 66.27 % 0.05 s GPU @ >3.5 Ghz (Python)
253 DASS 72.31 % 81.85 % 65.99 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
254 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 71.76 % 83.07 % 65.73 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
255 PointPainting
This method makes use of Velodyne laser scans.
 71.70 % 82.11 % 67.08 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
256 PI-SECOND code 71.46 % 81.62 % 66.26 % 0.05 s GPU @ >3.5 Ghz (Python + C/C++)
257 AEPF 71.22 % 81.43 % 66.58 % 0.05 s GPU @ 2.5 Ghz (Python)
258 WS3D
This method makes use of Velodyne laser scans.
 70.59 % 80.99 % 64.23 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
259 F-PointNet
This method makes use of Velodyne laser scans.
 code 69.79 % 82.19 % 60.59 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
260 EOTL code 69.13 % 79.97 % 58.57 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
261 UberATG-ContFuse
This method makes use of Velodyne laser scans.
 68.78 % 83.68 % 61.67 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
262 CPD++(unsupervised) code 67.90 % 84.20 % 62.53 % 0.1 s GPU @ >3.5 Ghz (Python)
263 MLOD
This method makes use of Velodyne laser scans.
 code 67.76 % 77.24 % 62.05 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
264 DSGN++
This method uses stereo information.
 code 67.37 % 83.21 % 59.91 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
265 DMF
This method uses stereo information.
 67.33 % 77.55 % 62.44 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
266 AVOD
This method makes use of Velodyne laser scans.
 code 66.47 % 76.39 % 60.23 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
267 StereoDistill 66.39 % 81.66 % 57.39 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
268 MMLAB LIGA-Stereo
This method uses stereo information.
 code 64.66 % 81.39 % 57.22 % 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.
269 BirdNet+
This method makes use of Velodyne laser scans.
 code 64.04 % 76.15 % 59.79 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
270 MV3D
This method makes use of Velodyne laser scans.
 63.63 % 74.97 % 54.00 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
271 SNVC
This method uses stereo information.
 code 61.34 % 78.54 % 54.23 % 1 s GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
272 RCD 60.56 % 70.54 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
273 SeSame-point w/score code 56.92 % 74.30 % 48.14 % N/A s 1 core @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
274 SeSame-point w/score code 56.92 % 74.30 % 48.14 % N/A s GPU @ 1.5 Ghz (Python)
275 A3DODWTDA
This method makes use of Velodyne laser scans.
 code 56.82 % 62.84 % 48.12 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
276 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
 code 54.88 % 68.38 % 49.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
277 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
 54.54 % 68.35 % 49.16 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
278 CDN
This method uses stereo information.
 code 54.22 % 74.52 % 46.36 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
279 CG-Stereo
This method uses stereo information.
 53.58 % 74.39 % 46.50 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
280 DSGN
This method uses stereo information.
 code 52.18 % 73.50 % 45.14 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
281 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 51.85 % 70.14 % 50.03 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
282 Complexer-YOLO
This method makes use of Velodyne laser scans.
 47.34 % 55.93 % 42.60 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
283 SeSame-voxel w/score code 47.14 % 61.57 % 41.06 % N/A s GPU @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
284 CPD(unsupervised) code 47.04 % 68.57 % 44.13 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
H. Wu, S. Zhao, X. Huang, C. Wen, X. Li and C. Wang: Commonsense Prototype for Outdoor Unsupervised 3D Object Detection. CVPR 2024.
285 ESGN
This method uses stereo information.
 46.39 % 65.80 % 38.42 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
286 Disp R-CNN (velo)
This method uses stereo information.
 code 45.78 % 68.21 % 37.73 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
287 CDN-PL++
This method uses stereo information.
 44.86 % 64.31 % 38.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
288 Disp R-CNN
This method uses stereo information.
 code 43.27 % 67.02 % 36.43 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
289 Pseudo-LiDAR++
This method uses stereo information.
 code 42.43 % 61.11 % 36.99 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
290 YOLOStereo3D
This method uses stereo information.
 code 41.25 % 65.68 % 30.42 % 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.
291 RT3D-GMP
This method uses stereo information.
 38.76 % 45.79 % 30.00 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
292 ZoomNet
This method uses stereo information.
 code 38.64 % 55.98 % 30.97 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
293 OC Stereo
This method uses stereo information.
 code 37.60 % 55.15 % 30.25 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
294 SST [st]
This method uses stereo information.
 35.49 % 57.02 % 31.03 % 1 s 1 core @ 2.5 Ghz (Python)
295 Pseudo-Lidar
This method uses stereo information.
 code 34.05 % 54.53 % 28.25 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
296 Stereo CenterNet
This method uses stereo information.
 31.30 % 49.94 % 25.62 % 0.04 s GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
297 Stereo R-CNN
This method uses stereo information.
 code 30.23 % 47.58 % 23.72 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
298 BirdNet
This method makes use of Velodyne laser scans.
 27.26 % 40.99 % 25.32 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
299 DA3D+KM3D+v2-99 26.80 % 34.72 % 23.05 % 0.120s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
300 CIE + DM3D 25.02 % 35.96 % 21.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Ananimities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
301 monodetrnext-a 24.14 % 29.94 % 23.79 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
302 RT3DStereo
This method uses stereo information.
 23.28 % 29.90 % 18.96 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
303 DA3D+KM3D code 22.08 % 30.83 % 19.20 % 0.02 s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
304 monodetrnext-f 21.69 % 27.21 % 21.16 % 0.03 s GPU @ 2.5 Ghz (Python)
305 MonoTAKD V2 21.26 % 29.86 % 18.27 % 0.1 s 1 core @ 2.5 Ghz (Python)
306 RD3D 21.19 % 32.02 % 18.80 % 0.1 s 1 core @ 2.5 Ghz (Python)
307 CIE 20.95 % 31.55 % 17.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
308 MonoMH code 20.88 % 29.12 % 17.93 % 0.04 s 1 core @ 2.5 Ghz (Python)
309 DA3D 20.47 % 27.76 % 17.89 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
310 zqd 20.19 % 32.74 % 17.04 % 0.1 s 1 core @ 2.5 Ghz (Python)
311 Sample code 19.49 % 25.75 % 15.70 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
312 MonoTAKD 19.42 % 27.91 % 16.51 % 0.1 s 1 core @ 2.5 Ghz (Python)
313 MonoLSS 19.15 % 26.11 % 16.94 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
314 RT3D
This method makes use of Velodyne laser scans.
 19.14 % 23.74 % 18.86 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
315 MonoAFKD 19.06 % 26.07 % 16.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
316 H3 19.01 % 28.07 % 16.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
317 zqd_test2 19.00 % 31.56 % 16.47 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
318 NeurOCS 18.94 % 29.89 % 15.90 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Min, B. Zhuang, S. Schulter, B. Liu, E. Dunn and M. Chandraker: NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization. CVPR 2023.
319 MonoLiG code 18.86 % 24.90 % 16.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
320 CMKD code 18.69 % 28.55 % 16.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
321 Mix-Teaching code 18.54 % 26.89 % 15.79 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
322 StereoFENet
This method uses stereo information.
 18.41 % 29.14 % 14.20 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
323 Occlude3D code 18.20 % 23.71 % 15.18 % 0.01 s 1 core @ 2.5 Ghz (Python)
324 SHUD 18.18 % 28.41 % 15.11 % 0.04 s 1 core @ 2.5 Ghz (Python)
325 PS-SVDM 18.13 % 29.22 % 15.35 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
326 SH3D 18.12 % 26.80 % 15.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
327 MonoSample (DID-M3D) code 18.05 % 28.63 % 15.19 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
J. Qiao, B. Liu, J. Yang, B. Wang, S. Xiu, X. Du and X. Nie: MonoSample: Synthetic 3D Data Augmentation Method in Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2024.
328 TBD 17.97 % 28.50 % 15.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
329 LPCG-Monoflex code 17.80 % 25.56 % 15.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
330 PS-fld code 17.74 % 23.74 % 15.14 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
331 MonoSKD code 17.35 % 28.43 % 15.01 % 0.04 s 1 core @ 2.5 Ghz (Python)
S. Wang and J. Zheng: MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficient. ECAI 2023.
332 zqd_test 17.27 % 26.84 % 14.91 % 0.2 s 1 core @ 2.5 Ghz (Python)
333 MonoDDE 17.14 % 24.93 % 15.10 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
334 MonoNeRD code 17.13 % 22.75 % 15.63 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
335 OPA-3D code 17.05 % 24.60 % 14.25 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
336 Mobile Stereo R-CNN
This method uses stereo information.
 17.04 % 26.97 % 13.26 % 1.8 s NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
337 DD3D code 16.87 % 23.19 % 14.36 % 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) .
338 ADD code 16.81 % 25.61 % 13.79 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Wu, Y. Wu, J. Pu, X. Li and X. Wang: Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection. AAAI2023 .
339 MonoSGC 16.77 % 27.01 % 14.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
340 MonoUNI code 16.73 % 24.75 % 13.49 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
341 MonoCD code 16.59 % 25.53 % 14.53 % n/a s 1 core @ 2.5 Ghz (Python)
L. Yan, P. Yan, S. Xiong, X. Xiang and Y. Tan: MonoCD: Monocular 3D Object Detection with Complementary Depths. CVPR 2024.
342 FDGNet code 16.53 % 27.22 % 13.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
343 MSFENet code 16.49 % 26.30 % 13.55 % 0.1 s 1 core @ 2.5 Ghz (Python)
344 DID-M3D code 16.29 % 24.40 % 13.75 % 0.04 s 1 core @ 2.5 Ghz (Python)
L. Peng, X. Wu, Z. Yang, H. Liu and D. Cai: DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection. ECCV 2022.
345 MonoDETR code 16.26 % 24.52 % 13.93 % 0.04 s 1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.
346 MonoFRD 16.24 % 21.11 % 14.97 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
347 DCD code 15.90 % 23.81 % 13.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Y. Li, Y. Chen, J. He and Z. Zhang: Densely Constrained Depth Estimator for Monocular 3D Object Detection. European Conference on Computer Vision 2022.
348 LLW 15.40 % 26.90 % 12.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
349 MonoDTR 15.39 % 21.99 % 12.73 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
350 GUPNet code 15.02 % 22.26 % 13.12 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
351 Cube R-CNN code 15.01 % 23.59 % 12.56 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
352 HomoLoss(monoflex) code 14.94 % 21.75 % 13.07 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
353 MonoSIM_v2 14.74 % 21.69 % 13.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
354 SGM3D code 14.65 % 22.46 % 12.97 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
355 MonoDSSMs-A 14.55 % 21.47 % 11.78 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
D. Kiet Dang Vu: MonoDSSMs: Efficient Monocular 3D Object Detection with Depth-Aware State Space Models. Computer Vision - ACCV 2024 - 17th Asian Conference on Computer Vision, HaNoi, VietNam, December 8-10, 2024, Proceedings 2024.
356 MDSNet 14.46 % 24.30 % 11.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
357 DEVIANT code 14.46 % 21.88 % 11.89 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
358 DLE code 14.33 % 24.23 % 10.30 % 0.06 s NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
359 AutoShape code 14.17 % 22.47 % 11.36 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
360 MonoDSSMs-M 14.15 % 19.80 % 11.56 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
D. Kiet Dang Vu: MonoDSSMs: Efficient Monocular 3D Object Detection with Depth-Aware State Space Models. Computer Vision - ACCV 2024 - 17th Asian Conference on Computer Vision, HaNoi, VietNam, December 8-10, 2024, Proceedings 2024.
361 MonoFlex 13.89 % 19.94 % 12.07 % 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.
362 MonoEF 13.87 % 21.29 % 11.71 % 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.
363 MonoRCNN++ code 13.72 % 20.08 % 11.34 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
364 DFR-Net 13.63 % 19.40 % 10.35 % 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.
365 PS-SVDM 13.49 % 20.83 % 11.18 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
366 CaDDN code 13.41 % 19.17 % 11.46 % 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.
367 PCT code 13.37 % 21.00 % 11.31 % 0.045 s 1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
368 Ground-Aware code 13.25 % 21.65 % 9.91 % 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.
369 FMF-occlusion-net 13.12 % 20.28 % 9.56 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
370 Aug3D-RPN 12.99 % 17.82 % 9.78 % 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.
371 HomoLoss(imvoxelnet) code 12.99 % 20.10 % 10.50 % 0.20 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
372 DDMP-3D 12.78 % 19.71 % 9.80 % 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.
373 mdab 12.74 % 18.62 % 11.10 % 22 s 1 core @ 2.5 Ghz (C/C++)
374 Kinematic3D code 12.72 % 19.07 % 9.17 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
375 MonoRCNN code 12.65 % 18.36 % 10.03 % 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.
376 GrooMeD-NMS code 12.32 % 18.10 % 9.65 % 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.
377 MonoRUn code 12.30 % 19.65 % 10.58 % 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.
378 monodle code 12.26 % 17.23 % 10.29 % 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 .
379 YoloMono3D code 12.06 % 18.28 % 8.42 % 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.
380 IAFA 12.01 % 17.81 % 10.61 % 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.
381 MonOAPC 12.00 % 18.77 % 9.75 % 0035 s 1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
382 GAC3D 12.00 % 17.75 % 9.15 % 0.25 s 1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
383 CMAN 11.87 % 17.77 % 9.16 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
384 PGD-FCOS3D code 11.76 % 19.05 % 9.39 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
385 D4LCN code 11.72 % 16.65 % 9.51 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
386 SAKD-MR-Res18 11.65 % 18.38 % 9.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
387 KM3D code 11.45 % 16.73 % 9.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
388 BEVHeight++ code 11.26 % 16.69 % 9.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, T. Tang, J. Li, P. Chen, K. Yuan, L. Wang, Y. Huang, X. Zhang and K. Yu: Bevheight++: Toward robust visual centric 3d object detection. arXiv preprint arXiv:2309.16179 2023.
389 RefinedMPL 11.14 % 18.09 % 8.94 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
390 PatchNet code 11.12 % 15.68 % 10.17 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
391 ImVoxelNet code 10.97 % 17.15 % 9.15 % 0.2 s GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
392 AM3D 10.74 % 16.50 % 9.52 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
393 RTM3D code 10.34 % 14.41 % 8.77 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
394 MonoPair 9.99 % 13.04 % 8.65 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
395 mdab 9.99 % 14.70 % 8.65 % 22 s 1 core @ 2.5 Ghz (Python)
396 Neighbor-Vote 9.90 % 15.57 % 8.89 % 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.
397 SMOKE code 9.76 % 14.03 % 7.84 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
398 M3D-RPN code 9.71 % 14.76 % 7.42 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
399 QD-3DT
This is an online method (no batch processing).
 code 9.33 % 12.81 % 7.86 % 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.
400 TopNet-HighRes
This method makes use of Velodyne laser scans.
 9.28 % 12.67 % 7.95 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
401 MonoCInIS 7.94 % 15.82 % 6.68 % 0,13 s GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
402 Plane-Constraints code 7.88 % 11.29 % 6.48 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
403 SS3D 7.68 % 10.78 % 6.51 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
404 MonoCInIS 7.66 % 15.21 % 6.24 % 0,14 s GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
405 Mono3D_PLiDAR code 7.50 % 10.76 % 6.10 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
406 mdab 7.47 % 11.55 % 6.27 % 0.02 s 1 core @ 2.5 Ghz (Python)
407 MonoPSR code 7.25 % 10.76 % 5.85 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
408 Decoupled-3D 7.02 % 11.08 % 5.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
409 mdab 6.94 % 10.52 % 5.18 % 0.02 s 1 core @ 2.5 Ghz (Python)
410 VoxelJones code 6.35 % 7.39 % 5.80 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
411 MonoGRNet code 5.74 % 9.61 % 4.25 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
412 A3DODWTDA (image) code 5.27 % 6.88 % 4.45 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
413 MonoFENet 5.14 % 8.35 % 4.10 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
414 TLNet (Stereo)
This method uses stereo information.
 code 4.37 % 7.64 % 3.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
415 CSoR
This method makes use of Velodyne laser scans.
 4.06 % 5.61 % 3.17 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
416 Shift R-CNN (mono) code 3.87 % 6.88 % 2.83 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
417 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
418 SparVox3D 3.20 % 5.27 % 2.56 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
419 TopNet-UncEst
This method makes use of Velodyne laser scans.
 3.02 % 3.24 % 2.26 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
420 GS3D 2.90 % 4.47 % 2.47 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
421 3D-GCK 2.52 % 3.27 % 2.11 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
422 WeakM3D code 2.26 % 5.03 % 1.63 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
423 ROI-10D 2.02 % 4.32 % 1.46 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
424 FQNet 1.51 % 2.77 % 1.01 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
425 3D-SSMFCNN code 1.41 % 1.88 % 1.11 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
426 f3sd code 0.01 % 0.01 % 0.01 % 1.67 s 1 core @ 2.5 Ghz (C/C++)
427 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 CasA++ code 49.29 % 56.33 % 46.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
2 TED code 49.21 % 55.85 % 46.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
3 UPIDet code 48.77 % 55.59 % 46.12 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
4 VPFNet code 48.36 % 54.65 % 44.98 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
C. Wang, H. Chen, Y. Chen, P. Hsiao and L. Fu: VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2024.
5 LoGoNet code 47.43 % 53.07 % 45.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
6 CasA code 47.09 % 54.04 % 44.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
7 EQ-PVRCNN code 47.02 % 55.84 % 42.94 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
8 SDGUFusion 46.84 % 53.10 % 43.45 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
9 PiFeNet code 46.71 % 56.39 % 42.71 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.
10 USVLab BSAODet code 46.50 % 52.69 % 43.10 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
11 ACFNet 46.36 % 54.62 % 42.57 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
12 DPPFA-Net 46.14 % 53.58 % 42.59 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Wang, X. Kong, H. Nishikawa, Q. Lian and H. Tomiyama: Dynamic Point-Pixel Feature Alignment for Multi-modal 3D Object Detection. IEEE Internet of Things Journal 2023.
13 PillarHist 45.85 % 55.79 % 42.15 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
14 OGMMDet code 45.61 % 53.49 % 43.10 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
15 ANM code 45.61 % 53.49 % 43.10 % ANM ANM
16 CAT-Det 45.44 % 54.26 % 41.94 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
17 HotSpotNet 45.37 % 53.10 % 41.47 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
18 MLF-DET 45.29 % 50.86 % 42.05 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
19 LumiNet code 45.26 % 53.54 % 41.55 % 0.1 s 1 core @ 2.5 Ghz (Python)
20 vsis-PHNet 45.26 % 55.60 % 42.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
21 PHNetp 45.26 % 55.60 % 42.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
22 ACDet code 44.79 % 53.41 % 41.96 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
23 focalnet 44.74 % 53.27 % 42.63 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
24 SCNet3D 44.64 % 51.69 % 41.44 % 0.08 s 1 core @ 2.5 Ghz (Python)
25 AFFN-G 44.63 % 52.89 % 42.36 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
26 focalnet 44.63 % 52.89 % 42.36 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
27 GEFPN 44.63 % 52.89 % 42.36 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
28 GeVo 44.63 % 52.89 % 42.36 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
29 EPNet++ 44.38 % 52.79 % 41.29 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
30 TANet code 44.34 % 53.72 % 40.49 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
31 3DSSD code 44.27 % 54.64 % 40.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
32 R2Pfusion-Det 44.05 % 53.15 % 41.83 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
33 HA-PillarNet 43.98 % 51.83 % 41.61 % 0.05 s 1 core @ 2.5 Ghz (Python)
34 CGML 43.86 % 51.04 % 41.66 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
35 Point-GNN
This method makes use of Velodyne laser scans.
 code 43.77 % 51.92 % 40.14 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
36 3ONet 43.45 % 52.81 % 39.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
37 FIRM-Net 43.43 % 51.65 % 40.97 % 0.07 s 1 core @ 2.5 Ghz (Python)
38 F-ConvNet
This method makes use of Velodyne laser scans.
 code 43.38 % 52.16 % 38.80 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
39 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 43.35 % 53.10 % 40.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
40 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 43.29 % 52.17 % 40.29 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
41 FromVoxelToPoint code 43.28 % 51.80 % 40.71 % 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.
42 VMVS
This method makes use of Velodyne laser scans.
 43.27 % 53.44 % 39.51 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
43 SFA-GCL code 43.24 % 52.90 % 39.37 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
44 P2V-RCNN 43.19 % 50.91 % 40.81 % 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.
45 MGAF-3DSSD code 43.09 % 50.65 % 39.65 % 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.
46 SFA-GCL(baseline) code 43.08 % 52.46 % 40.67 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
47 SFA-GCL_dataaug code 43.07 % 52.72 % 39.23 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
48 DGEnhCL code 43.07 % 51.31 % 39.29 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
49 Frustum-PointPillars code 42.89 % 51.22 % 39.28 % 0.06 s 4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
50 KPTr 42.76 % 48.85 % 39.49 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
51 SFA-GCL(80) code 42.76 % 52.05 % 39.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
52 Fast-CLOCs 42.72 % 52.10 % 39.08 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
53 HMFI code 42.65 % 50.88 % 39.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
54 LCANet 42.63 % 49.74 % 40.20 % 1 s 1 core @ 2.5 Ghz (C/C++)
55 FocalsConv* 42.56 % 50.40 % 40.24 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
56 SFA-GCL(80, k=4) code 42.55 % 51.78 % 38.79 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
57 STD code 42.47 % 53.29 % 38.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
58 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 42.27 % 50.46 % 39.04 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
59 SemanticVoxels 42.19 % 50.90 % 39.52 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
60 F-PointNet
This method makes use of Velodyne laser scans.
 code 42.15 % 50.53 % 38.08 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
61 PASS-PV-RCNN-Plus 41.95 % 47.66 % 38.90 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
62 PointPillars
This method makes use of Velodyne laser scans.
 code 41.92 % 51.45 % 38.89 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
63 RAFDet 41.89 % 48.95 % 38.66 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
64 BPG3D 41.79 % 48.73 % 39.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
65 VPA 41.76 % 49.10 % 38.38 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
66 epBRM
This method makes use of Velodyne laser scans.
 code 41.52 % 49.17 % 39.08 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
67 Anonymous code 41.50 % 49.01 % 37.94 % 0.04 s 1 core @ 2.5 Ghz (Python)
68 LGNet-3classes code 41.45 % 47.88 % 38.63 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
69 SFA-GCL code 41.18 % 50.41 % 38.97 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
70 PG-RCNN code 41.04 % 47.99 % 38.71 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
71 IA-SSD (single) code 41.03 % 47.90 % 37.98 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
72 DFAF3D 40.99 % 47.58 % 37.65 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
73 PointPainting
This method makes use of Velodyne laser scans.
 40.97 % 50.32 % 37.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
74 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 40.89 % 46.97 % 38.80 % 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.
75 AFFN 40.85 % 46.71 % 38.54 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
76 MG 40.85 % 47.79 % 37.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 CG-SSD 40.64 % 47.35 % 37.71 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
78 L_SACINet 40.57 % 48.99 % 37.13 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
79 PDV code 40.56 % 47.80 % 38.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
80 MFB3D 40.40 % 46.46 % 38.20 % 0.14 s 1 core @ 2.5 Ghz (Python)
81 SVGA-Net 40.39 % 48.48 % 37.92 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
82 PV-RCNN-Plus 40.31 % 47.50 % 38.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
83 MPC3DNet 40.31 % 44.88 % 37.53 % 0.05 s GPU @ 1.5 Ghz (Python)
84 DiffCandiDet 40.27 % 49.24 % 37.99 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
85 test 40.27 % 45.91 % 38.03 % 0.04 s GPU @ 1.5 Ghz (Python + C/C++)
86 MLFusion-VS 40.25 % 46.60 % 38.30 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
87 CAIA_PRO code 40.20 % 47.70 % 37.69 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
88 EOTL code 40.11 % 48.65 % 35.99 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
89 PI-SECOND code 39.98 % 50.39 % 35.98 % 0.05 s GPU @ >3.5 Ghz (Python + C/C++)
90 voxelnext_pcdet 39.97 % 47.46 % 37.43 % 0.05 s 1 core @ 2.5 Ghz (Python)
91 M3DeTR code 39.94 % 45.70 % 37.66 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
92 centerpoint_pcdet 39.74 % 46.68 % 37.33 % 0.06 s 1 core @ 2.5 Ghz (Python)
93 AFFN-Ga 39.69 % 45.38 % 37.56 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
94 TF-PartA2 39.63 % 47.93 % 37.16 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
95 SRDL 39.43 % 47.30 % 36.99 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
96 Test_dif code 39.41 % 47.24 % 37.20 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
97 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 39.37 % 47.98 % 36.01 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
98 ARPNET 39.31 % 48.32 % 35.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
99 bs 39.28 % 46.29 % 37.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
100 BAPartA2S-4h 39.21 % 47.67 % 36.53 % 0.1 s 1 core @ 2.5 Ghz (Python)
101 GSG-FPS code 39.17 % 46.47 % 37.10 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
102 L-AUG 39.07 % 46.76 % 35.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
103 IA-SSD (multi) code 39.03 % 46.51 % 35.61 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
104 R50_SACINet 38.96 % 47.05 % 36.55 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
105 SIF 38.74 % 46.23 % 36.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
106 SCNet
This method makes use of Velodyne laser scans.
 38.66 % 47.83 % 35.70 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
107 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 38.58 % 46.33 % 35.71 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
108 XT-PartA2 38.22 % 46.24 % 35.51 % 0.1 s GPU @ >3.5 Ghz (Python)
109 HINTED code 37.75 % 47.33 % 34.10 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, W. Ye, H. Wu, S. Zhao, L. Xing, X. Huang, J. Deng, X. Li, C. Wen and C. Wang: HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely- Supervised 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
110 DVFENet 37.50 % 43.55 % 35.33 % 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.
111 MLOD
This method makes use of Velodyne laser scans.
 code 37.47 % 47.58 % 35.07 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
112 PCNet3D_Extension_V1 37.47 % 45.22 % 34.95 % 0.5 s GPU @ 3.5 Ghz (Python)
113 AEPF 37.46 % 44.97 % 34.51 % 0.05 s GPU @ 2.5 Ghz (Python)
114 PUDet 37.42 % 46.00 % 35.12 % 0.3 s GPU @ 2.5 Ghz (Python)
115 SeSame-voxel code 37.37 % 46.53 % 33.56 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
116 S-AT GCN 37.37 % 44.63 % 34.92 % 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.
117 VSAC 37.02 % 45.26 % 33.35 % 0.07 s 1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
118 PCNet3D 37.00 % 44.66 % 34.16 % 0.05 s GPU @ 3.5 Ghz (Python)
119 GraphAlign(ICCV2023) code 36.89 % 41.38 % 34.95 % 0.03 s GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
120 mm3d_PartA2 36.84 % 44.67 % 34.48 % 0.1 s GPU @ >3.5 Ghz (Python)
121 XView 36.79 % 42.44 % 34.96 % 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.
122 SecAtten 36.49 % 44.13 % 34.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 PFF3D
This method makes use of Velodyne laser scans.
 code 36.07 % 43.93 % 32.86 % 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.
124 VoxelFSD-S 35.71 % 42.78 % 33.26 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
125 focal 35.64 % 41.12 % 33.95 % 100 s 1 core @ 2.5 Ghz (Python)
126 DensePointPillars 35.38 % 42.76 % 32.63 % 0.03 s GPU @ 2.5 Ghz (Python)
127 SeSame-point code 35.34 % 42.29 % 33.02 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
128 MM_SECOND code 35.19 % 44.12 % 32.02 % 0.05 s GPU @ >3.5 Ghz (Python)
129 BirdNet+
This method makes use of Velodyne laser scans.
 code 35.06 % 41.55 % 32.93 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
130 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 34.59 % 42.27 % 31.37 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
131 PL++: PV-RCNN++
This method uses stereo information.
This method makes use of Velodyne laser scans.
 33.89 % 41.53 % 31.42 % 0.342 s RTX 4060Ti (Python)
132 DSGN++
This method uses stereo information.
 code 32.74 % 43.05 % 29.54 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
133 StereoDistill 32.23 % 44.12 % 28.95 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
134 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 31.46 % 37.99 % 29.46 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
135 T-SSD 31.36 % 39.36 % 29.14 % 0.04 1 core @ 2.0 Ghz (C/C++)
136 SeSame-pillar code 31.00 % 37.61 % 28.86 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
137 SparsePool code 30.38 % 37.84 % 26.94 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
138 MMLAB LIGA-Stereo
This method uses stereo information.
 code 30.00 % 40.46 % 27.07 % 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.
139 DMF
This method uses stereo information.
 29.77 % 37.21 % 27.62 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
140 SeSame-voxel w/score code 28.26 % 34.14 % 26.15 % N/A s GPU @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
141 SparsePool code 27.92 % 35.52 % 25.87 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
142 AVOD
This method makes use of Velodyne laser scans.
 code 27.86 % 36.10 % 25.76 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
143 SeSame-pillar w/scor code 27.23 % 33.87 % 25.27 % N/A s 1 core @ 2.5 Ghz (C/C++)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
144 CSW3D
This method makes use of Velodyne laser scans.
 26.64 % 33.75 % 23.34 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
145 PointRGBNet 26.40 % 34.77 % 24.03 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
146 Disp R-CNN (velo)
This method uses stereo information.
 code 25.80 % 37.12 % 22.04 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
147 Disp R-CNN
This method uses stereo information.
 code 25.40 % 35.75 % 21.79 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
148 CG-Stereo
This method uses stereo information.
 24.31 % 33.22 % 20.95 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
149 SeSame-point w/score code 23.33 % 31.13 % 20.07 % N/A s 1 core @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
150 SeSame-point w/score code 23.33 % 31.13 % 20.07 % N/A s GPU @ 1.5 Ghz (Python)
151 YOLOStereo3D
This method uses stereo information.
 code 19.75 % 28.49 % 16.48 % 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.
152 OC Stereo
This method uses stereo information.
 code 17.58 % 24.48 % 15.60 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
153 BirdNet
This method makes use of Velodyne laser scans.
 17.08 % 22.04 % 15.82 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
154 CPD++(unsupervised) code 15.58 % 17.64 % 14.60 % 0.1 s GPU @ >3.5 Ghz (Python)
155 DSGN
This method uses stereo information.
 code 15.55 % 20.53 % 14.15 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
156 Complexer-YOLO
This method makes use of Velodyne laser scans.
 13.96 % 17.60 % 12.70 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
157 MonoMH code 11.70 % 17.45 % 10.05 % 0.04 s 1 core @ 2.5 Ghz (Python)
158 RT3D-GMP
This method uses stereo information.
 11.41 % 16.23 % 10.12 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
159 MonoAFKD 11.32 % 17.15 % 9.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
160 MonoLSS 11.27 % 17.09 % 10.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
161 DD3D code 11.04 % 16.64 % 9.38 % 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) .
162 PS-fld code 10.82 % 16.95 % 9.26 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
163 CIE 10.53 % 16.19 % 8.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
164 OPA-3D code 10.49 % 15.65 % 8.80 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
165 MonoUNI code 10.34 % 15.78 % 8.74 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
166 ESGN
This method uses stereo information.
 10.27 % 14.05 % 9.02 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
167 MonoTAKD V2 10.27 % 16.09 % 8.76 % 0.1 s 1 core @ 2.5 Ghz (Python)
168 SST [st]
This method uses stereo information.
 10.21 % 15.39 % 8.85 % 1 s 1 core @ 2.5 Ghz (Python)
169 MonoDTR 10.18 % 15.33 % 8.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
170 MonoTAKD 10.00 % 14.88 % 8.49 % 0.1 s 1 core @ 2.5 Ghz (Python)
171 GUPNet code 9.76 % 14.95 % 8.41 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
172 MonoFRD 8.88 % 13.86 % 7.53 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
173 SGM3D code 8.81 % 13.99 % 7.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
174 CMKD code 8.79 % 13.94 % 7.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
175 CPD(unsupervised) code 8.66 % 10.87 % 7.83 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
H. Wu, S. Zhao, X. Huang, C. Wen, X. Li and C. Wang: Commonsense Prototype for Outdoor Unsupervised 3D Object Detection. CVPR 2024.
176 DEVIANT code 8.65 % 13.43 % 7.69 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
177 PS-SVDM 8.33 % 12.93 % 7.20 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
178 MonoNeRD code 8.26 % 13.20 % 7.02 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
179 mdab 8.22 % 12.88 % 6.91 % 22 s 1 core @ 2.5 Ghz (C/C++)
180 mdab 8.19 % 12.76 % 6.86 % 22 s 1 core @ 2.5 Ghz (Python)
181 CaDDN code 8.14 % 12.87 % 6.76 % 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.
182 SAKD-MR-Res18 8.06 % 12.62 % 6.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
183 LLW 7.96 % 12.99 % 6.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
184 MonoRCNN++ code 7.90 % 12.26 % 6.62 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
185 HomoLoss(monoflex) code 7.66 % 11.87 % 6.82 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
186 MonoSIM_v2 7.52 % 11.90 % 6.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
187 Mix-Teaching code 7.47 % 11.67 % 6.61 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
188 LPCG-Monoflex code 7.33 % 10.82 % 6.18 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
189 MonoDDE 7.32 % 11.13 % 6.67 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
190 RefinedMPL 7.18 % 11.14 % 5.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
191 MDSNet 7.09 % 10.68 % 6.06 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
192 Cube R-CNN code 6.95 % 11.17 % 5.87 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
193 PS-SVDM 6.93 % 11.16 % 5.96 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
194 TopNet-HighRes
This method makes use of Velodyne laser scans.
 6.92 % 10.40 % 6.63 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
195 MonoRUn code 6.78 % 10.88 % 5.83 % 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.
196 MonoPair 6.68 % 10.02 % 5.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
197 monodle code 6.55 % 9.64 % 5.44 % 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 .
198 DA3D+KM3D+v2-99 6.32 % 9.38 % 5.54 % 0.120s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
199 MonoFlex 6.31 % 9.43 % 5.26 % 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.
200 SH3D 6.05 % 9.03 % 5.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
201 MonOAPC 5.87 % 8.75 % 4.84 % 0035 s 1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
202 mdab 5.80 % 8.86 % 4.63 % 0.02 s 1 core @ 2.5 Ghz (Python)
203 FMF-occlusion-net 5.23 % 7.62 % 4.28 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
204 Aug3D-RPN 4.71 % 6.01 % 3.87 % 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.
205 Shift R-CNN (mono) code 4.66 % 7.95 % 4.16 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
206 MonoPSR code 4.00 % 6.12 % 3.30 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
207 DA3D+KM3D code 3.64 % 5.60 % 3.10 % 0.02 s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
208 DFR-Net 3.62 % 6.09 % 3.39 % 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.
209 DDMP-3D 3.55 % 4.93 % 3.01 % 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.
210 M3D-RPN code 3.48 % 4.92 % 2.94 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
211 D4LCN code 3.42 % 4.55 % 2.83 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
212 CMAN 3.41 % 4.62 % 2.87 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
213 QD-3DT
This is an online method (no batch processing).
 code 3.37 % 5.53 % 3.02 % 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.
214 DA3D 2.95 % 4.62 % 2.58 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
215 MonoEF 2.79 % 4.27 % 2.21 % 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.
216 RT3DStereo
This method uses stereo information.
 2.45 % 3.28 % 2.35 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
217 MonoLiG code 1.94 % 2.89 % 1.91 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
218 TopNet-UncEst
This method makes use of Velodyne laser scans.
 1.87 % 3.42 % 1.73 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
219 SS3D 1.78 % 2.31 % 1.48 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
220 PGD-FCOS3D code 1.49 % 2.28 % 1.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
221 SparVox3D 1.35 % 1.93 % 1.04 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
222 Plane-Constraints code 1.09 % 1.73 % 1.04 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
223 mdab 1.07 % 1.92 % 1.03 % 0.02 s 1 core @ 2.5 Ghz (Python)
224 f3sd code 0.00 % 0.00 % 0.00 % 1.67 s 1 core @ 2.5 Ghz (C/C++)
225 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 UPIDet code 74.32 % 86.74 % 67.45 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
2 TED code 74.12 % 88.82 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
3 CasA++ code 73.79 % 87.76 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
4 CasA code 73.47 % 87.91 % 66.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
5 LoGoNet code 71.70 % 84.47 % 64.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
6 MLF-DET 70.71 % 83.31 % 63.71 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
7 USVLab BSAODet code 70.48 % 83.17 % 62.46 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
8 HMFI code 70.37 % 84.02 % 62.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
9 VPA 70.32 % 83.38 % 62.64 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
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10 vsis-PHNet 70.29 % 84.85 % 62.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
11 EQ-PVRCNN code 69.10 % 85.41 % 62.30 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
12 PHNetc 69.04 % 83.42 % 61.29 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
13 OGMMDet code 68.96 % 82.39 % 62.19 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
14 ANM code 68.96 % 82.39 % 62.19 % ANM ANM
15 CAT-Det 68.81 % 83.68 % 61.45 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
16 BtcDet
This method makes use of Velodyne laser scans.
 code 68.68 % 82.81 % 61.81 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
17 RagNet3D code 68.55 % 83.84 % 61.94 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Chen, Y. Han, Z. Yan, J. Qian, J. Li and J. Yang: Ragnet3d: Learning Distinguishable Representation for Pooled Grids in 3d Object Detection. Available at SSRN 4979473 .
18 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 68.54 % 82.19 % 61.33 % 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.
19 PASS-PV-RCNN-Plus 68.45 % 80.43 % 60.93 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
20 ACFNet 68.37 % 84.29 % 62.08 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
21 3ONet 68.37 % 82.34 % 60.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
22 AFFN 68.33 % 83.01 % 61.07 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
23 GEFPN 68.33 % 83.01 % 61.07 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
24 Anonymous code 68.09 % 82.31 % 60.11 % 0.04 s 1 core @ 2.5 Ghz (Python)
25 focalnet 67.94 % 80.51 % 61.71 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
26 AFFN-G 67.87 % 80.49 % 61.57 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
27 focalnet 67.87 % 80.49 % 61.57 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
28 DiffCandiDet 67.84 % 83.87 % 60.56 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
29 PG-RCNN code 67.82 % 82.77 % 61.25 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
30 PDV code 67.81 % 83.04 % 60.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
31 RangeIoUDet
This method makes use of Velodyne laser scans.
 67.77 % 83.12 % 60.26 % 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.
32 SCNet3D 67.55 % 82.11 % 59.65 % 0.08 s 1 core @ 2.5 Ghz (Python)
33 MPC3DNet 67.55 % 80.92 % 60.36 % 0.05 s GPU @ 1.5 Ghz (Python)
34 PV-RCNN-Plus 67.46 % 80.34 % 60.48 % 1 s 1 core @ 2.5 Ghz (C/C++)
35 KPTr 67.29 % 80.73 % 60.38 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
36 test 67.20 % 83.24 % 59.97 % 0.04 s GPU @ 1.5 Ghz (Python + C/C++)
37 SPG_mini
This method makes use of Velodyne laser scans.
 code 66.96 % 80.21 % 60.50 % 0.09 s GPU @ 2.5 Ghz (Python)
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.
38 BPG3D 66.85 % 83.90 % 59.97 % 0.05 s 1 core @ 2.5 Ghz (Python)
39 M3DeTR code 66.74 % 83.83 % 59.03 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
40 ACDet code 66.61 % 83.80 % 59.99 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
41 IA-SSD (single) code 66.25 % 82.36 % 59.70 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
42 LGNet-3classes code 66.05 % 78.87 % 59.42 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
43 HotSpotNet 65.95 % 82.59 % 59.00 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
44 DFAF3D 65.86 % 82.09 % 59.02 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
45 FIRM-Net 65.72 % 81.53 % 58.03 % 0.07 s 1 core @ 2.5 Ghz (Python)
46 LCANet 65.65 % 81.08 % 59.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
47 SDGUFusion 65.61 % 79.69 % 59.56 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
48 MFB3D 65.45 % 79.80 % 59.43 % 0.14 s 1 core @ 2.5 Ghz (Python)
49 RAFDet 65.36 % 82.25 % 58.75 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
50 Fast-CLOCs 65.31 % 82.83 % 57.43 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
51 AFFN-Ga 65.27 % 80.19 % 59.40 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
52 F-ConvNet
This method makes use of Velodyne laser scans.
 code 65.07 % 81.98 % 56.54 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
53 CGML 65.06 % 81.52 % 59.68 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
54 CG-SSD 64.58 % 78.53 % 57.66 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
55 MLFusion-VS 64.48 % 78.76 % 58.61 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
56 GraphAlign(ICCV2023) code 64.43 % 78.42 % 58.71 % 0.03 s GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
57 focal 64.36 % 78.88 % 58.60 % 100 s 1 core @ 2.5 Ghz (Python)
58 GeVo 64.36 % 78.88 % 58.60 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
59 SFA-GCL(80) code 64.23 % 80.94 % 55.74 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
60 3DSSD code 64.10 % 82.48 % 56.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
61 VPFNet code 64.10 % 77.64 % 58.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
C. Wang, H. Chen, Y. Chen, P. Hsiao and L. Fu: VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2024.
62 SFA-GCL(80, k=4) code 63.96 % 80.35 % 55.62 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
63 SFA-GCL code 63.84 % 80.39 % 57.20 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
64 PointPainting
This method makes use of Velodyne laser scans.
 63.78 % 77.63 % 55.89 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
65 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 63.71 % 78.60 % 57.65 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
66 R2Pfusion-Det 63.70 % 80.74 % 56.91 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
67 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 63.52 % 79.17 % 56.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
68 Point-GNN
This method makes use of Velodyne laser scans.
 code 63.48 % 78.60 % 57.08 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
69 CAIA_PRO code 63.47 % 78.24 % 56.12 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
70 MGAF-3DSSD code 63.43 % 80.64 % 55.15 % 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.
71 FromVoxelToPoint code 63.41 % 81.49 % 56.40 % 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.
72 bs 63.30 % 78.70 % 57.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 P2V-RCNN 63.13 % 78.62 % 56.81 % 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.
74 BAPartA2S-4h 63.07 % 79.31 % 57.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
75 HINTED code 63.01 % 76.21 % 55.85 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, W. Ye, H. Wu, S. Zhao, L. Xing, X. Huang, J. Deng, X. Li, C. Wen and C. Wang: HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely- Supervised 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
76 XT-PartA2 62.82 % 76.88 % 56.99 % 0.1 s GPU @ >3.5 Ghz (Python)
77 H^23D R-CNN code 62.74 % 78.67 % 55.78 % 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.
78 TF-PartA2 62.68 % 78.61 % 57.06 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
79 LumiNet code 62.31 % 80.43 % 55.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
80 SVGA-Net 62.28 % 78.58 % 54.88 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
81 Test_dif code 62.04 % 77.79 % 55.86 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
82 SRDL 62.02 % 77.35 % 55.52 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
83 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 62.00 % 77.36 % 55.40 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
84 DVFENet 62.00 % 78.73 % 55.18 % 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.
85 IA-SSD (multi) code 61.94 % 78.35 % 55.70 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
86 mm3d_PartA2 61.83 % 76.92 % 55.51 % 0.1 s GPU @ >3.5 Ghz (Python)
87 voxelnext_pcdet 61.74 % 78.18 % 54.68 % 0.05 s 1 core @ 2.5 Ghz (Python)
88 S-AT GCN 61.70 % 75.24 % 55.32 % 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.
89 FocalsConv* 61.63 % 75.43 % 57.13 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
90 SIF 61.61 % 77.13 % 55.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
91 STD code 61.59 % 78.69 % 55.30 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
92 GSG-FPS code 61.47 % 76.84 % 55.36 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
93 PI-SECOND code 61.06 % 77.31 % 55.07 % 0.05 s GPU @ >3.5 Ghz (Python + C/C++)
94 DGEnhCL code 60.89 % 76.99 % 52.65 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
95 T-SSD 60.70 % 76.05 % 55.28 % 0.04 1 core @ 2.0 Ghz (C/C++)
96 SecAtten 60.65 % 73.85 % 54.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
97 L_SACINet 60.52 % 75.65 % 54.64 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
98 Res3DNet 60.47 % 76.11 % 53.77 % 0.05 s GPU @ 3.5 Ghz (Python)
99 HA-PillarNet 60.42 % 75.47 % 55.89 % 0.05 s 1 core @ 2.5 Ghz (Python)
100 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 60.30 % 75.42 % 53.81 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
101 VoxelFSD-S 60.04 % 76.63 % 53.72 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
102 MG 59.86 % 78.72 % 53.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
103 centerpoint_pcdet 59.80 % 76.27 % 53.02 % 0.06 s 1 core @ 2.5 Ghz (Python)
104 SFA-GCL code 59.76 % 78.02 % 53.06 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
105 EPNet++ 59.71 % 76.15 % 53.67 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
106 XView 59.55 % 77.24 % 53.47 % 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.
107 SFA-GCL_dataaug code 59.45 % 77.51 % 52.94 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
108 TANet code 59.44 % 75.70 % 52.53 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
109 L-AUG 59.30 % 73.32 % 53.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
110 EOTL code 58.96 % 75.20 % 50.41 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
111 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 58.82 % 74.96 % 52.53 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
112 SFA-GCL(baseline) code 58.80 % 76.26 % 52.21 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
113 PCNet3D_Extension_V1 58.67 % 76.16 % 52.02 % 0.5 s GPU @ 3.5 Ghz (Python)
114 PointPillars
This method makes use of Velodyne laser scans.
 code 58.65 % 77.10 % 51.92 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
115 ARPNET 58.20 % 74.21 % 52.13 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
116 R50_SACINet 58.06 % 72.08 % 52.43 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
117 MM_SECOND code 57.81 % 73.41 % 51.68 % 0.05 s GPU @ >3.5 Ghz (Python)
118 DensePointPillars 57.36 % 70.95 % 50.98 % 0.03 s GPU @ 2.5 Ghz (Python)
119 PCNet3D 57.28 % 74.15 % 51.02 % 0.05 s GPU @ 3.5 Ghz (Python)
120 VSAC 56.99 % 75.54 % 50.90 % 0.07 s 1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
121 epBRM
This method makes use of Velodyne laser scans.
 code 56.13 % 72.08 % 49.91 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
122 F-PointNet
This method makes use of Velodyne laser scans.
 code 56.12 % 72.27 % 49.01 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
123 SeSame-point code 54.56 % 69.55 % 48.34 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
124 SeSame-voxel code 54.36 % 70.97 % 48.66 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
125 BirdNet+
This method makes use of Velodyne laser scans.
 code 53.84 % 65.67 % 49.06 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
126 PUDet 53.53 % 69.10 % 47.48 % 0.3 s GPU @ 2.5 Ghz (Python)
127 PointRGBNet 52.15 % 67.05 % 46.78 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
128 SeSame-pillar code 51.74 % 64.55 % 46.13 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
129 PillarHist 51.62 % 66.64 % 45.30 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
130 DMF
This method uses stereo information.
 51.33 % 65.51 % 45.05 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
131 PiFeNet code 51.10 % 67.50 % 44.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.
132 SCNet
This method makes use of Velodyne laser scans.
 50.79 % 67.98 % 45.15 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
133 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 50.55 % 63.76 % 44.93 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
134 AEPF 50.16 % 63.21 % 44.89 % 0.05 s GPU @ 2.5 Ghz (Python)
135 MLOD
This method makes use of Velodyne laser scans.
 code 49.43 % 68.81 % 42.84 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
136 PL++: PV-RCNN++
This method uses stereo information.
This method makes use of Velodyne laser scans.
 48.97 % 62.80 % 42.80 % 0.342 s RTX 4060Ti (Python)
137 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 47.72 % 67.38 % 42.89 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
138 PFF3D
This method makes use of Velodyne laser scans.
 code 46.78 % 63.27 % 41.37 % 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.
139 StereoDistill 44.02 % 63.96 % 39.19 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
140 DSGN++
This method uses stereo information.
 code 43.90 % 62.82 % 39.21 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
141 AVOD
This method makes use of Velodyne laser scans.
 code 42.08 % 57.19 % 38.29 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
142 SeSame-voxel w/score code 40.05 % 53.37 % 35.71 % N/A s GPU @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
143 SparsePool code 37.33 % 52.61 % 33.39 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
144 MMLAB LIGA-Stereo
This method uses stereo information.
 code 36.86 % 54.44 % 32.06 % 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.
145 CPD++(unsupervised) code 33.84 % 50.49 % 29.35 % 0.1 s GPU @ >3.5 Ghz (Python)
146 SparsePool code 32.61 % 40.87 % 29.05 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
147 CG-Stereo
This method uses stereo information.
 30.89 % 47.40 % 27.23 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
148 BirdNet
This method makes use of Velodyne laser scans.
 30.25 % 43.98 % 27.21 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
149 Disp R-CNN (velo)
This method uses stereo information.
 code 24.40 % 40.05 % 21.12 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
150 Disp R-CNN
This method uses stereo information.
 code 24.40 % 40.04 % 21.12 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
151 Complexer-YOLO
This method makes use of Velodyne laser scans.
 18.53 % 24.27 % 17.31 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
152 DSGN
This method uses stereo information.
 code 18.17 % 27.76 % 16.21 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
153 OC Stereo
This method uses stereo information.
 code 16.63 % 29.40 % 14.72 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
154 SeSame-pillar w/scor code 14.29 % 11.47 % 12.57 % N/A s 1 core @ 2.5 Ghz (C/C++)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
155 RT3D-GMP
This method uses stereo information.
 12.99 % 18.31 % 10.63 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
156 SST [st]
This method uses stereo information.
 12.77 % 22.45 % 11.59 % 1 s 1 core @ 2.5 Ghz (Python)
157 MonoTAKD V2 8.78 % 14.59 % 7.50 % 0.1 s 1 core @ 2.5 Ghz (Python)
158 MonoTAKD 8.51 % 14.12 % 7.23 % 0.1 s 1 core @ 2.5 Ghz (Python)
159 SeSame-point w/score code 8.31 % 9.99 % 6.87 % N/A s 1 core @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
160 SeSame-point w/score code 8.31 % 9.99 % 6.87 % N/A s GPU @ 1.5 Ghz (Python)
161 ESGN
This method uses stereo information.
 7.69 % 13.84 % 6.75 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
162 CMKD code 6.67 % 12.52 % 6.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
163 PS-fld code 6.18 % 11.22 % 5.21 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
164 MonoLiG code 5.24 % 8.14 % 4.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
165 DA3D+KM3D+v2-99 5.11 % 8.58 % 4.48 % 0.120s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
166 Mix-Teaching code 4.91 % 8.04 % 4.15 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
167 DD3D code 4.79 % 7.52 % 4.22 % 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) .
168 MonoPSR code 4.74 % 8.37 % 3.68 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
169 MonoSIM_v2 4.64 % 8.23 % 3.91 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
170 MonoMH code 4.62 % 7.32 % 4.23 % 0.04 s 1 core @ 2.5 Ghz (Python)
171 PS-SVDM 4.57 % 7.98 % 3.66 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
172 TopNet-UncEst
This method makes use of Velodyne laser scans.
 4.54 % 7.13 % 3.81 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
173 CPD(unsupervised) code 4.43 % 6.75 % 3.84 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
H. Wu, S. Zhao, X. Huang, C. Wen, X. Li and C. Wang: Commonsense Prototype for Outdoor Unsupervised 3D Object Detection. CVPR 2024.
174 LPCG-Monoflex code 4.38 % 6.98 % 3.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
175 MonoLSS 4.34 % 7.23 % 3.92 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
176 MonoUNI code 4.28 % 7.34 % 3.78 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
177 MonoAFKD 4.27 % 7.11 % 3.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
178 Plane-Constraints code 4.22 % 7.72 % 3.36 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
179 mdab 3.80 % 6.79 % 3.35 % 22 s 1 core @ 2.5 Ghz (Python)
180 MonoDDE 3.78 % 5.94 % 3.33 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
181 DFR-Net 3.58 % 5.69 % 3.10 % 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.
182 HomoLoss(monoflex) code 3.50 % 5.48 % 2.99 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
183 OPA-3D code 3.45 % 5.16 % 2.86 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
184 CaDDN code 3.41 % 7.00 % 3.30 % 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.
185 RT3DStereo
This method uses stereo information.
 3.37 % 5.29 % 2.57 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
186 MonoFRD 3.33 % 6.38 % 3.12 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
187 MonoDTR 3.27 % 5.05 % 3.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
188 GUPNet code 3.21 % 5.58 % 2.66 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
189 DEVIANT code 3.13 % 5.05 % 2.59 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
190 CIE 3.09 % 5.62 % 2.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
191 SH3D 3.00 % 5.29 % 2.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
192 SGM3D code 2.92 % 5.49 % 2.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
193 PS-SVDM 2.92 % 5.56 % 2.36 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
194 mdab 2.85 % 5.32 % 2.28 % 0.02 s 1 core @ 2.5 Ghz (Python)
195 MonOAPC 2.74 % 4.46 % 2.14 % 0035 s 1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
196 MDSNet 2.68 % 5.37 % 2.22 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
197 Cube R-CNN code 2.67 % 3.65 % 2.28 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
198 monodle code 2.66 % 4.59 % 2.45 % 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 .
199 DDMP-3D 2.50 % 4.18 % 2.32 % 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.
200 MonoNeRD code 2.48 % 4.73 % 2.16 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
201 Aug3D-RPN 2.43 % 4.36 % 2.55 % 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.
202 QD-3DT
This is an online method (no batch processing).
 code 2.39 % 4.16 % 1.85 % 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.
203 MonoFlex 2.35 % 4.17 % 2.04 % 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.
204 mdab 2.31 % 4.19 % 2.01 % 22 s 1 core @ 2.5 Ghz (C/C++)
205 MonoPair 2.12 % 3.79 % 1.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
206 SAKD-MR-Res18 1.87 % 3.50 % 1.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
207 DA3D 1.86 % 3.37 % 1.48 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
208 RefinedMPL 1.82 % 3.23 % 1.77 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
209 MonoRCNN++ code 1.81 % 3.17 % 1.75 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
210 TopNet-HighRes
This method makes use of Velodyne laser scans.
 1.67 % 2.49 % 1.88 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
211 D4LCN code 1.67 % 2.45 % 1.36 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
212 FMF-occlusion-net 1.60 % 1.87 % 1.66 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
213 SS3D 1.45 % 2.80 % 1.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
214 PGD-FCOS3D code 1.38 % 2.81 % 1.20 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
215 DA3D+KM3D code 1.37 % 2.79 % 1.32 % 0.02 s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
216 LLW 1.28 % 2.28 % 1.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
217 mdab 1.12 % 3.10 % 1.24 % 0.02 s 1 core @ 2.5 Ghz (Python)
218 CMAN 1.05 % 1.59 % 1.11 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
219 MonoEF 0.92 % 1.80 % 0.71 % 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.
220 M3D-RPN code 0.65 % 0.94 % 0.47 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
221 MonoRUn code 0.61 % 1.01 % 0.48 % 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.
222 Shift R-CNN (mono) code 0.29 % 0.48 % 0.31 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
223 f3sd code 0.01 % 0.02 % 0.01 % 1.67 s 1 core @ 2.5 Ghz (C/C++)
224 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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