Bird's Eye View Evaluation 2017


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

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

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 VirConv-S code 93.52 % 95.99 % 90.38 % 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 93.40 % 95.34 % 89.07 % 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 92.65 % 96.11 % 89.69 % 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 WWW 92.26 % 95.96 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 GraR-Po code 92.12 % 95.79 % 87.11 % 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.
6 TSSTDet 92.11 % 95.80 % 89.23 % 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.
7 LDRFusion 92.09 % 95.58 % 87.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 MPCF code 92.07 % 95.92 % 87.29 % 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.
9 TED code 92.05 % 95.44 % 87.30 % 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.
10 TRTConv-L 92.04 % 95.55 % 87.23 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
11 BFT3D 92.04 % 95.88 % 85.24 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
12 SQD++ 92.03 % 95.72 % 88.92 % 0.08 s GPU @ >3.5 Ghz (Python)
13 None 92.03 % 95.72 % 88.92 % 0.05 1 core @ 2.5 Ghz (C/C++)
14 mm3d 92.01 % 95.66 % 87.20 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
15 ViKIENet 91.87 % 95.69 % 88.99 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Z. Yu, B. Qiu and A. Khong: ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network. CVPR 2025.
16 VPFNet code 91.86 % 93.02 % 86.94 % 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.
17 SFD code 91.85 % 95.64 % 86.83 % 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.
18 mat3D 91.85 % 95.49 % 87.07 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
19 SE-SSD
This method makes use of Velodyne laser scans.
 code 91.84 % 95.68 % 86.72 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
20 ACFNet 91.78 % 92.91 % 87.06 % 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 GraR-Vo code 91.72 % 95.27 % 86.51 % 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.
22 TRTConv-T 91.70 % 95.63 % 89.00 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
23 PVT-SSD 91.63 % 95.23 % 86.43 % 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.
24 SCDA-Net 91.61 % 95.45 % 89.16 % - s 1 core @ 2.5 Ghz (C/C++)
25 3D-AWARE 91.60 % 95.54 % 88.95 % 0.1 s 1 core @ 2.5 Ghz (Python)
26 SPANet 91.59 % 95.59 % 86.53 % 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.
27 ViKIENet-R 91.56 % 94.87 % 88.55 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Z. Yu, B. Qiu and A. Khong: ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network. CVPR 2025.
28 RM3D 91.55 % 94.77 % 88.65 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
29 CasA code 91.54 % 95.19 % 86.82 % 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.
30 LoGoNet code 91.52 % 95.48 % 87.09 % 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.
31 GraR-Pi code 91.52 % 95.06 % 86.42 % 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.
32 P3GMF 91.50 % 94.82 % 88.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
33 UPIDet code 91.36 % 92.96 % 86.80 % 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.
34 BADet code 91.32 % 95.23 % 86.48 % 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.
35 CasA++ code 91.22 % 94.57 % 88.43 % 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.
36 ImagePG 91.14 % 94.91 % 86.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 MuStD 91.13 % 94.62 % 88.28 % 67 ms >8 cores @ 2.5 Ghz (Python)
38 3D HANet code 91.13 % 94.33 % 86.33 % 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.
39 SpaA 91.05 % 94.69 % 86.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 MPC3DNet 91.03 % 95.56 % 86.36 % 0.05 s GPU @ 1.5 Ghz (Python)
41 SA-SSD code 91.03 % 95.03 % 85.96 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
42 L-AUG 91.00 % 94.52 % 88.08 % 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.
43 3D Dual-Fusion code 90.86 % 93.08 % 86.44 % 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.
44 GraphAlign(ICCV2023) code 90.73 % 94.46 % 88.34 % 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.
45 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 90.65 % 94.98 % 86.14 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
46 SQD code 90.63 % 95.44 % 88.04 % 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.
47 VPFNet code 90.52 % 93.94 % 86.25 % 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.
48 PDV code 90.48 % 94.56 % 86.23 % 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.
49 RobusTor3D 90.46 % 94.69 % 87.94 % ... s 1 core @ 2.5 Ghz (C/C++)
50 M3DeTR code 90.37 % 94.41 % 85.98 % 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.
51 VoTr-TSD code 90.34 % 94.03 % 86.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
52 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 90.13 % 92.42 % 85.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
53 LumiNet code 90.13 % 95.79 % 85.06 % 0.1 s 1 core @ 2.5 Ghz (Python)
54 XView 90.12 % 92.27 % 85.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
55 GraR-VoI code 90.10 % 95.69 % 86.85 % 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.
56 WinMamba code 90.07 % 93.46 % 87.94 % 0.1 s 1 core @ 2.5 Ghz (Python)
57 CAT-Det 90.07 % 92.59 % 85.82 % 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.
58 3ONet 90.07 % 95.87 % 85.09 % 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.
59 New_VLGCL code 89.98 % 94.00 % 85.98 % 0.4 s 1 core @ 2.5 Ghz (Python)
60 PointVit V1 89.95 % 95.94 % 82.40 % .006 s 1 core @ 2.5 Ghz (Python + C/C++)
61 SVGA-Net 89.88 % 92.07 % 85.59 % 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.
62 EBM3DOD code 89.86 % 95.64 % 84.56 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
63 ... code 89.84 % 94.65 % 85.85 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
64 CIA-SSD
This method makes use of Velodyne laser scans.
 code 89.84 % 93.74 % 82.39 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
65 MLF-DET 89.82 % 93.38 % 84.78 % 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.
66 CLOCs_PVCas code 89.80 % 93.05 % 86.57 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
67 Voxel RCNN-Focal* code 89.76 % 92.06 % 86.01 % 0.2 s 1 core @ 2.5 Ghz (Python)
68 GLENet-VR code 89.76 % 93.48 % 84.89 % 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.
69 RDIoU code 89.75 % 94.90 % 84.67 % 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.
70 PointVit V2 89.67 % 93.59 % 82.12 % .006 s 1 core @ 2.5 Ghz (Python + C/C++)
71 EBM3DOD baseline code 89.63 % 95.44 % 84.34 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
72 PointVit P1 89.57 % 93.44 % 82.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
73 3D-CVF at SPA
This method makes use of Velodyne laser scans.
 code 89.56 % 93.52 % 82.45 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
74 OcTr 89.56 % 93.08 % 86.74 % 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.
75 VLGCL_NoText code 89.55 % 92.22 % 85.79 % 0.3 s 1 core @ 2.5 Ghz (Python)
76 Struc info fusion II 89.54 % 95.26 % 82.31 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
77 SASA
This method makes use of Velodyne laser scans.
 code 89.51 % 92.87 % 86.35 % 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.
78 Fast-CLOCs 89.49 % 93.03 % 86.40 % 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.
79 IA-SSD (single) code 89.48 % 93.14 % 84.42 % 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.
80 CLOCs code 89.48 % 92.91 % 86.42 % 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.
81 DPFusion code 89.47 % 95.20 % 86.49 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
Y. Mo, Y. Wu, J. Zhao, Y. Hu, J. Wang and J. Yan: Enhancing LiDAR Point Features with Foundation Model Priors for 3D Object Detection. ITSC 2025.
82 PA3DNet 89.46 % 93.11 % 84.60 % 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.
83 PG-RCNN code 89.46 % 93.39 % 86.54 % 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.
84 DFAF3D 89.45 % 93.14 % 84.22 % 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.
85 DVF-V 89.42 % 93.12 % 86.50 % 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.
86 CGML 89.41 % 92.20 % 85.77 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
87 Struc info fusion I 89.38 % 94.91 % 84.29 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
88 R2Pfusion-Det 89.37 % 92.96 % 86.70 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
89 BtcDet
This method makes use of Velodyne laser scans.
 code 89.34 % 92.81 % 84.55 % 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.
90 IA-SSD (multi) code 89.33 % 92.79 % 84.35 % 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.
91 CAIA_PRO code 89.27 % 92.84 % 84.26 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
92 ACDet code 89.21 % 92.87 % 85.80 % 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.
93 DVF-PV 89.20 % 93.08 % 86.28 % 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.
94 FIRM-Net_SCF+ 89.20 % 92.57 % 86.35 % 0.07 s 1 core @ 2.5 Ghz (Python)
95 STD code 89.19 % 94.74 % 86.42 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
96 FIRM-Net-SCF 89.19 % 92.56 % 86.34 % 0.07 s 1 core @ 2.5 Ghz (Python)
97 Point-GNN
This method makes use of Velodyne laser scans.
 code 89.17 % 93.11 % 83.90 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
98 HMFI code 89.17 % 93.04 % 86.37 % 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.
99 SSL-PointGNN code 89.16 % 92.92 % 83.99 % 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.
100 CEF code 89.16 % 92.51 % 84.27 % 0.03 s 1 core @ 2.5 Ghz (Python)
101 SPG_mini
This method makes use of Velodyne laser scans.
 code 89.12 % 92.80 % 86.27 % 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.
102 EQ-PVRCNN code 89.09 % 94.55 % 86.42 % 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.
103 VoxSeT code 89.07 % 92.70 % 86.29 % 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.
104 3DSSD code 89.02 % 92.66 % 85.86 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
105 RagNet3D code 89.01 % 92.87 % 86.36 % 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 .
106 EPNet++ 89.00 % 95.41 % 85.73 % 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.
107 Focals Conv code 89.00 % 92.67 % 86.33 % 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.
108 USVLab BSAODet code 88.90 % 92.66 % 86.23 % 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.
109 H^23D R-CNN code 88.87 % 92.85 % 86.07 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
110 Pyramid R-CNN 88.84 % 92.19 % 86.21 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
111 CityBrainLab-CT3D code 88.83 % 92.36 % 84.07 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
112 Voxel R-CNN code 88.83 % 94.85 % 86.13 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
113 HVNet 88.82 % 92.83 % 83.38 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
114 GD-MAE 88.82 % 94.22 % 83.54 % 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.
115 dsvd+vx 88.81 % 90.54 % 84.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
116 SPG
This method makes use of Velodyne laser scans.
 code 88.70 % 94.33 % 85.98 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
117 SIENet code 88.65 % 92.38 % 86.03 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
118 P2V-RCNN 88.63 % 92.72 % 86.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
119 FromVoxelToPoint code 88.61 % 92.23 % 86.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
120 RangeIoUDet
This method makes use of Velodyne laser scans.
 88.59 % 92.28 % 85.83 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
121 2025AAAI-SSLfusion code 88.54 % 94.64 % 85.82 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
122 EPNet code 88.47 % 94.22 % 83.69 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
123 CenterNet3D 88.46 % 91.80 % 83.62 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
124 FARP-Net code 88.45 % 91.20 % 86.01 % 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.
125 BVIFusion+ 88.42 % 92.02 % 85.73 % 0.09 s 1 core @ 2.5 Ghz (Python)
126 RangeRCNN
This method makes use of Velodyne laser scans.
 88.40 % 92.15 % 85.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
127 Patches
This method makes use of Velodyne laser scans.
 88.39 % 92.72 % 83.19 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
128 3D IoU-Net 88.38 % 94.76 % 81.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
129 StructuralIF 88.38 % 91.78 % 85.67 % 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.
130 PASS-PV-RCNN-Plus 88.37 % 92.17 % 85.75 % 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.
131 SFA_IGCL_Focalsconv* code 88.35 % 92.51 % 86.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
132 Voxel RCNN* code 88.33 % 92.26 % 85.58 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
133 BFT3D_easy 88.26 % 94.19 % 80.99 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
134 Fade-kd 88.25 % 92.16 % 83.34 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
135 CLOCs_SecCas 88.23 % 91.16 % 82.63 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
136 UberATG-MMF
This method makes use of Velodyne laser scans.
 88.21 % 93.67 % 81.99 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
137 Patches - EMP
This method makes use of Velodyne laser scans.
 88.17 % 94.49 % 84.75 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
138 SRDL 88.17 % 92.01 % 85.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
139 FocalsConv* 88.13 % 92.10 % 85.83 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
140 PointPainting
This method makes use of Velodyne laser scans.
 88.11 % 92.45 % 83.36 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
141 SERCNN
This method makes use of Velodyne laser scans.
 88.10 % 94.11 % 83.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
142 Associate-3Ddet code 88.09 % 91.40 % 82.96 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
143 HotSpotNet 88.09 % 94.06 % 83.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
144 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 88.08 % 91.90 % 85.35 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
145 SVFMamba code 88.06 % 91.61 % 84.96 % N/A s 1 core @ 2.5 Ghz (C/C++)
146 UberATG-HDNET
This method makes use of Velodyne laser scans.
 87.98 % 93.13 % 81.23 % 0.05 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
147 work6_new1 87.93 % 91.28 % 84.58 % 0.5 s GPU @ 2.5 Ghz (Python)
148 Fade 3D code 87.85 % 93.53 % 82.75 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
W. Ye, Q. Xia, H. Wu, Z. Dong, R. Zhong, C. Wang and C. Wen: Fade3D: Fast and Deployable 3D Object Detection for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems 2025.
149 HMNet 87.85 % 91.89 % 84.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
150 Fast Point R-CNN
This method makes use of Velodyne laser scans.
 87.84 % 90.87 % 80.52 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
151 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 87.79 % 91.70 % 84.61 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
152 SIF 87.76 % 91.44 % 85.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
153 MVAF-Net code 87.73 % 91.95 % 85.00 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
154 NoText_VLGCL code 87.71 % 93.84 % 85.21 % 0.2 s 1 core @ 2.5 Ghz (Python)
155 MSFASA-3DNet 87.70 % 91.35 % 84.38 % 0.03 s GPU @ 2.5 Ghz (Python)
156 geo-pillars 87.68 % 91.50 % 84.06 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
157 DVFENet 87.68 % 90.93 % 84.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
158 S-AT GCN 87.68 % 90.85 % 84.20 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
159 RangeDet (Official) code 87.67 % 90.93 % 82.92 % 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.
160 XPillars
This method makes use of Velodyne laser scans.
 87.63 % 90.82 % 82.78 % 0.02 s GPU @ 2.5 Ghz (Python)
161 MODet
This method makes use of Velodyne laser scans.
 87.56 % 90.80 % 82.69 % 0.05 s GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
162 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 87.53 % 91.99 % 81.03 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
163 DSFNet 87.52 % 90.95 % 82.11 % 0.05 s GPU @ 2.5 Ghz (Python)
164 CS3D 87.51 % 90.76 % 84.22 % 0.5 s 1 core @ 2.5 Ghz (Python)
165 SeSame-point code 87.49 % 90.84 % 83.77 % 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.
166 PointRGCN 87.49 % 91.63 % 80.73 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
167 MGAF-3DSSD code 87.47 % 92.70 % 82.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
168 PC-CNN-V2
This method makes use of Velodyne laser scans.
 87.40 % 91.19 % 79.35 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
169 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 87.39 % 92.13 % 82.72 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
170 Sem-Aug
This method makes use of Velodyne laser scans.
 87.37 % 93.35 % 82.43 % 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.
171 MAFF-Net(DAF-Pillar) 87.34 % 90.79 % 77.66 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
172 Harmonic PointPillar code 87.28 % 90.89 % 82.54 % 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.
173 PASS-PointPillar 87.23 % 91.07 % 81.98 % 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.
174 HRI-VoxelFPN 87.21 % 92.75 % 79.82 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
175 PCNet3D++ 87.17 % 90.13 % 83.10 % 0.05 s GPU @ 3.5 Ghz (Python)
176 epBRM
This method makes use of Velodyne laser scans.
 code 87.13 % 90.70 % 81.92 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
177 R_Pillar 87.07 % 90.50 % 82.05 % 0.05 s GPU @ 2.5 Ghz (Python)
178 AARMOD 87.02 % 92.59 % 79.64 % 0.1 s 1 core @ 2.5 Ghz (Python)
179 M3DNet 86.97 % 90.27 % 81.95 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
180 SARPNET 86.92 % 92.21 % 81.68 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
181 SA V1 86.91 % 89.49 % 83.82 % 0.5 s GPU @ 2.5 Ghz (Python)
182 SeSame-pillar code 86.88 % 90.61 % 81.93 % 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.
183 ARPNET 86.81 % 90.06 % 79.41 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
184 C-GCN 86.78 % 91.11 % 80.09 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
185 PointPillars
This method makes use of Velodyne laser scans.
 code 86.56 % 90.07 % 82.81 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
186 TANet code 86.54 % 91.58 % 81.19 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
187 T-SSD 86.50 % 92.50 % 81.30 % 0.04 1 core @ 2.0 Ghz (C/C++)
188 SCNet
This method makes use of Velodyne laser scans.
 86.48 % 90.07 % 81.30 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
189 SegVoxelNet 86.37 % 91.62 % 83.04 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
190 DensePointPillars 86.31 % 92.13 % 81.12 % 0.03 s GPU @ 2.5 Ghz (Python)
191 3D IoU Loss
This method makes use of Velodyne laser scans.
 86.22 % 91.36 % 81.20 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
192 SeSame-pillar w/scor code 86.11 % 90.43 % 81.38 % 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.
193 R-GCN 86.05 % 91.91 % 81.05 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
194 UberATG-PIXOR++
This method makes use of Velodyne laser scans.
 86.01 % 93.28 % 80.11 % 0.035 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
195 HINTED code 86.01 % 90.61 % 79.29 % 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.
196 PointPillars_mmdet3d 85.96 % 91.25 % 80.82 % 0.03 s 1 core @ 2.5 Ghz (Python)
197 PL++: PV-RCNN++
This method uses stereo information.
This method makes use of Velodyne laser scans.
 85.89 % 91.76 % 81.29 % 0.342 s RTX 4060Ti (Python)
X. Gong, X. Huang, S. Chen and B. Zhang: Enhancing 3D Detection Accuracy in Autonomous Driving through Pseudo-LiDAR Augmentation and Downsampling. 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) 2024.
198 Sem-Aug-PointRCNN++ 85.88 % 91.68 % 83.37 % 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.
199 DASS 85.85 % 91.74 % 80.97 % 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.
200 F-ConvNet
This method makes use of Velodyne laser scans.
 code 85.84 % 91.51 % 76.11 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
201 PI-RCNN 85.81 % 91.44 % 81.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
202 PointRGBNet 85.73 % 91.39 % 80.68 % 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.
203 SeSame-voxel code 85.62 % 89.86 % 80.95 % 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.
204 UberATG-ContFuse
This method makes use of Velodyne laser scans.
 85.35 % 94.07 % 75.88 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
205 PFF3D
This method makes use of Velodyne laser scans.
 code 85.08 % 89.61 % 80.42 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
206 AVOD
This method makes use of Velodyne laser scans.
 code 84.95 % 89.75 % 78.32 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
207 WS3D
This method makes use of Velodyne laser scans.
 84.93 % 90.96 % 77.96 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
208 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 84.82 % 90.99 % 79.62 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
209 F-PointNet
This method makes use of Velodyne laser scans.
 code 84.67 % 91.17 % 74.77 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
210 mmFUSION code 84.60 % 90.35 % 79.82 % 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.
211 3DBN
This method makes use of Velodyne laser scans.
 83.94 % 89.66 % 76.50 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
212 EOTL code 83.14 % 89.10 % 71.41 % 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.
213 MLOD
This method makes use of Velodyne laser scans.
 code 82.68 % 90.25 % 77.97 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
214 BirdNet+
This method makes use of Velodyne laser scans.
 code 81.85 % 87.43 % 75.36 % 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.
215 CPD++(unsupervised) code 81.62 % 90.22 % 76.56 % 0.1 s GPU @ >3.5 Ghz (Python)
216 DMF
This method uses stereo information.
 80.29 % 84.64 % 76.05 % 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.
217 UberATG-PIXOR
This method makes use of Velodyne laser scans.
 80.01 % 83.97 % 74.31 % 0.035 s TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
218 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
 78.98 % 86.49 % 72.23 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
219 DSGN++
This method uses stereo information.
 code 78.94 % 88.55 % 69.74 % 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.
220 MV3D
This method makes use of Velodyne laser scans.
 78.93 % 86.62 % 69.80 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
221 StereoDistill 78.59 % 89.03 % 69.34 % 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.
222 MMLAB LIGA-Stereo
This method uses stereo information.
 code 76.78 % 88.15 % 67.40 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
223 RCD 75.83 % 82.26 % 69.61 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
224 LaserNet 74.52 % 79.19 % 68.45 % 12 ms GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
225 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
 code 73.80 % 84.61 % 65.59 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
226 SNVC
This method uses stereo information.
 code 73.61 % 86.88 % 64.49 % 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.
227 A3DODWTDA
This method makes use of Velodyne laser scans.
 code 73.26 % 79.58 % 62.77 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
228 Complexer-YOLO
This method makes use of Velodyne laser scans.
 68.96 % 77.24 % 64.95 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
229 TopNet-Retina
This method makes use of Velodyne laser scans.
 68.16 % 80.16 % 63.43 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
230 SeSame-point w/score code 67.18 % 83.44 % 57.68 % 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.
231 CG-Stereo
This method uses stereo information.
 66.44 % 85.29 % 58.95 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
232 PLUME
This method uses stereo information.
 66.27 % 82.97 % 56.70 % 0.15 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Yang, R. Hu, M. Liang and R. Urtasun: PLUME: Efficient 3D Object Detection from Stereo Images. IROS 2021.
233 CDN
This method uses stereo information.
 code 66.24 % 83.32 % 57.65 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
234 DSGN
This method uses stereo information.
 code 65.05 % 82.90 % 56.60 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
235 CPD(unsupervised) code 64.77 % 81.68 % 61.60 % 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.
236 TopNet-DecayRate
This method makes use of Velodyne laser scans.
 64.60 % 79.74 % 58.04 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
237 SeSame-voxel w/score code 63.36 % 71.98 % 57.52 % 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.
238 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 63.33 % 84.80 % 61.23 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
239 3D FCN
This method makes use of Velodyne laser scans.
 61.67 % 70.62 % 55.61 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
240 CDN-PL++
This method uses stereo information.
 61.04 % 81.27 % 52.84 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
241 BirdNet
This method makes use of Velodyne laser scans.
 59.83 % 84.17 % 57.35 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
242 TopNet-UncEst
This method makes use of Velodyne laser scans.
 59.67 % 72.05 % 51.67 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
243 RT3D-GMP
This method uses stereo information.
 59.00 % 69.14 % 45.49 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
244 Disp R-CNN (velo)
This method uses stereo information.
 code 58.62 % 79.76 % 47.73 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
245 ESGN
This method uses stereo information.
 58.12 % 78.10 % 49.28 % 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.
246 Pseudo-LiDAR++
This method uses stereo information.
 code 58.01 % 78.31 % 51.25 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
247 Disp R-CNN
This method uses stereo information.
 code 57.98 % 79.61 % 47.09 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
248 ZoomNet
This method uses stereo information.
 code 54.91 % 72.94 % 44.14 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
249 VoxelJones code 53.96 % 66.21 % 47.66 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
250 DDStereo
This method uses stereo information.
 53.59 % 73.59 % 45.01 % 0.02 s GPU @ 2.5 Ghz (Python)
251 TopNet-HighRes
This method makes use of Velodyne laser scans.
 53.05 % 67.84 % 46.99 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
252 OC Stereo
This method uses stereo information.
 code 51.47 % 68.89 % 42.97 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
253 YOLOStereo3D
This method uses stereo information.
 code 50.28 % 76.10 % 36.86 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
254 RT3DStereo
This method uses stereo information.
 46.82 % 58.81 % 38.38 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
255 Pseudo-Lidar
This method uses stereo information.
 code 45.00 % 67.30 % 38.40 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
256 RT3D
This method makes use of Velodyne laser scans.
 44.00 % 56.44 % 42.34 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
257 Stereo CenterNet
This method uses stereo information.
 42.12 % 62.97 % 35.37 % 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.
258 Stereo R-CNN
This method uses stereo information.
 code 41.31 % 61.92 % 33.42 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
259 DA3D+KM3D+v2-99 code 34.88 % 44.27 % 30.29 % 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.
260 test_det 33.51 % 35.50 % 30.93 % -1 s 1 core @ 2.5 Ghz (C/C++)
261 CIE + DM3D 33.13 % 46.17 % 28.80 % 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.
262 StereoFENet
This method uses stereo information.
 32.96 % 49.29 % 25.90 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
263 MonoDTF 28.92 % 42.67 % 25.89 % 0.1 s 1 core @ 2.5 Ghz (Python)
Anonymities: Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance- Level Spatial Refinement. arXiv preprint arXiv:2412.19165 2024.
264 Mobile Stereo R-CNN
This method uses stereo information.
 28.78 % 44.51 % 22.30 % 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.
265 DA3D+KM3D code 28.71 % 39.50 % 25.20 % 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.
266 CIE 28.50 % 41.41 % 23.88 % 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.
267 MonoMH code 28.06 % 37.85 % 24.53 % 0.04 s 1 core @ 2.5 Ghz (Python)
268 M3D 27.60 % 38.62 % 24.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
269 STLM3D 27.53 % 37.93 % 24.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
270 MonoCoP-Car 27.04 % 38.30 % 23.59 % 0.01 s GPU @ 2.5 Ghz (Python)
271 DA3D code 26.92 % 36.83 % 23.41 % 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.
272 MonoLiG code 26.83 % 35.73 % 24.24 % 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.
273 MonoLSPF 26.72 % 37.13 % 23.10 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
274 MonoCoP 26.60 % 37.58 % 23.15 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
275 MM3D 26.58 % 38.80 % 23.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
276 MDD-M3D-X 26.57 % 38.33 % 23.74 % 0.01 s 1 core @ 2.5 Ghz (Python)
277 SSM3D 26.48 % 38.25 % 23.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
278 M5_3D 26.48 % 38.60 % 22.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
279 MonoCLUE 26.15 % 36.15 % 22.81 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
280 MonoHPE-Mask 26.11 % 35.43 % 22.83 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
281 AM 26.05 % 36.38 % 22.87 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
282 MonOri code 26.00 % 37.86 % 23.10 % 0.03 s 4 cores @ 2.5 Ghz (Python)
H. Yao, P. Han, J. Chen, Z. Wang, Y. Qiu, X. Wang, Y. wang, X. Chai, C. Cao and W. Jin: MonOri: Orientation-Guided PnP for Monocular 3-D Object Detection. IEEE Transactions on Neural Networks and Learning Systems 2025.
283 MonoLSS 25.95 % 34.89 % 22.59 % 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.
284 MonoGAD 25.92 % 35.88 % 22.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
285 H3 25.89 % 35.59 % 22.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
286 DetAny3D code 25.83 % 36.71 % 21.94 % 0.58 s 1 core @ 2.5 Ghz (Python)
287 MonoAFKD 25.83 % 34.57 % 22.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
288 CMKD code 25.82 % 38.98 % 22.80 % 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.
289 IDEAL-M3D 60% 25.44 % 35.33 % 22.25 % 0.04 s 1 core @ 2.5 Ghz (Python)
290 AMNet+DDAD15M code 25.40 % 34.68 % 22.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
H. Pan, Y. Jia, J. Wang and W. Sun: MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods. IEEE Transactions on Intelligent Transportation Systems 2025.
291 MonoDLGD 25.30 % 36.63 % 23.13 % 0.04 s GPU @ 2.5 Ghz (Python)
292 MonoHPE 25.27 % 36.51 % 22.77 % 0.04 s 1 core @ 2.5 Ghz (Python)
293 UniCuboid 25.25 % 35.64 % 22.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
294 GATE3D code 25.06 % 33.94 % 22.04 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
295 BEVHeight++ code 24.90 % 37.51 % 20.93 % 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.
296 MonoGeo code 24.88 % 35.62 % 21.69 % 0.14 s GPU @ 2.5 Ghz (Python)
297 AMNet code 24.84 % 34.71 % 22.14 % 0.03 s GPU @ 1.0 Ghz (Python)
H. Pan, Y. Jia, J. Wang and W. Sun: MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods. IEEE Transactions on Intelligent Transportation Systems 2025.
298 MonoVQD 24.82 % 35.77 % 21.37 % 0.02 s 1 core @ 2.5 Ghz (Python)
299 PS-SVDM 24.82 % 38.18 % 20.89 % 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.
300 LPCG-Monoflex code 24.81 % 35.96 % 21.86 % 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.
301 Monohan 24.80 % 32.49 % 21.70 % 0.05 s 1 core @ 2.5 Ghz (Python)
302 MonoSC 24.74 % 32.93 % 21.38 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
303 MonoCLUE_all 24.59 % 35.83 % 21.36 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
304 MonoGeo code 24.51 % 35.60 % 21.37 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
305 NeurOCS 24.49 % 37.27 % 20.89 % 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.
306 Mix-Teaching code 24.23 % 35.74 % 20.80 % 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.
307 fdaa11 24.23 % 35.08 % 20.99 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
308 MonoCLUE 24.20 % 34.68 % 21.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
309 MonoSKD code 24.08 % 37.12 % 20.37 % 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.
310 MonoSample (DID-M3D) code 23.94 % 37.64 % 20.46 % 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.
311 PS-fld code 23.76 % 32.64 % 20.64 % 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.
312 ADD code 23.58 % 35.20 % 20.08 % 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 .
313 MonoNeRD code 23.46 % 31.13 % 20.97 % 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.
314 MonoDDE 23.46 % 33.58 % 20.37 % 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.
315 DD3D code 23.41 % 32.35 % 20.42 % 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) .
316 MonoUNI code 23.05 % 33.28 % 19.39 % 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.
317 MonoCD code 22.81 % 33.41 % 19.57 % 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.
318 MonoFRD 22.77 % 29.65 % 20.41 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
Z. Gong, Y. Zhao, F. Zhang, G. Gui, B. Chen, L. Yu, H. Wang, C. Yang and W. Gui: Color intuitive feature guided depth-height fusion and volume rendering for monocular 3D object detection. IEEE Transactions on Intelligent Vehicles(Major Revison) 2024.
319 DID-M3D code 22.76 % 32.95 % 19.83 % 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.
320 OPA-3D code 22.53 % 33.54 % 19.22 % 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.
321 DCD code 21.50 % 32.55 % 18.25 % 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.
322 MonoDETR code 21.45 % 32.20 % 18.68 % 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.
323 SGM3D code 21.37 % 31.49 % 18.43 % 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.
324 Cube R-CNN code 21.20 % 31.70 % 18.43 % 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.
325 GUPNet code 21.19 % 30.29 % 18.20 % 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.
326 HomoLoss(monoflex) code 20.68 % 29.60 % 17.81 % 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.
327 DEVIANT code 20.44 % 29.65 % 17.43 % 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.
328 MonoDTR 20.38 % 28.59 % 17.14 % 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.
329 MDSNet 20.14 % 32.81 % 15.77 % 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.
330 AutoShape code 20.08 % 30.66 % 15.95 % 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.
331 temp 20.05 % 28.51 % 17.11 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
332 MonoFlex 19.75 % 28.23 % 16.89 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
333 MonoEF 19.70 % 29.03 % 17.26 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
334 MonOAPC 19.67 % 28.91 % 16.99 % 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.
335 MonoDSSMs-M 19.59 % 28.29 % 16.34 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
K. Vu, T. Tran and D. Nguyen: MonoDSSMs: Efficient Monocular 3D Object Detection with Depth-Aware State Space Models. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
336 MonoDSSMs-A 19.54 % 28.84 % 16.30 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
K. Vu, T. Tran and D. Nguyen: MonoDSSMs: Efficient Monocular 3D Object Detection with Depth-Aware State Space Models. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
337 HomoLoss(imvoxelnet) code 19.25 % 29.18 % 16.21 % 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.
338 DFR-Net 19.17 % 28.17 % 14.84 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
339 PS-SVDM 19.07 % 28.52 % 16.30 % 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.
340 DLE code 19.05 % 31.09 % 14.13 % 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.
341 PCT code 19.03 % 29.65 % 15.92 % 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.
342 CaDDN code 18.91 % 27.94 % 17.19 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
343 monodle code 18.89 % 24.79 % 16.00 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
344 Neighbor-Vote 18.65 % 27.39 % 16.54 % 0.1 s GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
345 MonoRCNN++ code 18.62 % 27.20 % 15.69 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
346 GrooMeD-NMS code 18.27 % 26.19 % 14.05 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
347 MonoRCNN code 18.11 % 25.48 % 14.10 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
348 Ground-Aware code 17.98 % 29.81 % 13.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
349 Aug3D-RPN 17.89 % 26.00 % 14.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
350 DDMP-3D 17.89 % 28.08 % 13.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
351 IAFA 17.88 % 25.88 % 15.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
352 mdab 17.74 % 26.42 % 15.71 % 0.02 s 1 core @ 2.5 Ghz (Python)
353 FMF-occlusion-net 17.60 % 27.39 % 13.25 % 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.
354 RefinedMPL 17.60 % 28.08 % 13.95 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
355 Kinematic3D code 17.52 % 26.69 % 13.10 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
356 MonoRUn code 17.34 % 27.94 % 15.24 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
357 AM3D 17.32 % 25.03 % 14.91 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
358 YoloMono3D code 17.15 % 26.79 % 12.56 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
359 CMAN 17.04 % 25.89 % 12.88 % 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.
360 GAC3D 16.93 % 25.80 % 12.50 % 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.
361 PatchNet code 16.86 % 22.97 % 14.97 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
362 PGD-FCOS3D code 16.51 % 26.89 % 13.49 % 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.
363 ImVoxelNet code 16.37 % 25.19 % 13.58 % 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.
364 KM3D code 16.20 % 23.44 % 14.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
365 D4LCN code 16.02 % 22.51 % 12.55 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
366 MonoPair 14.83 % 19.28 % 12.89 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
367 Decoupled-3D 14.82 % 23.16 % 11.25 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
368 QD-3DT
This is an online method (no batch processing).
 code 14.71 % 20.16 % 12.76 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
369 SMOKE code 14.49 % 20.83 % 12.75 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
370 RTM3D code 14.20 % 19.17 % 11.99 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
371 Mono3D_PLiDAR code 13.92 % 21.27 % 11.25 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
372 M3D-RPN code 13.67 % 21.02 % 10.23 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
373 CSoR
This method makes use of Velodyne laser scans.
 13.07 % 18.67 % 10.34 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
374 MonoPSR code 12.58 % 18.33 % 9.91 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
375 Plane-Constraints code 12.06 % 17.31 % 10.05 % 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.
376 MonoCInIS 11.64 % 22.28 % 9.95 % 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.
377 SS3D 11.52 % 16.33 % 9.93 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
378 MonoGRNet code 11.17 % 18.19 % 8.73 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
379 MonoFENet 11.03 % 17.03 % 9.05 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
380 MonoCInIS 10.96 % 20.42 % 9.23 % 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.
381 monospb 10.83 % 14.92 % 9.55 % 0.01 s 1 core @ 2.5 Ghz (Python)
382 A3DODWTDA (image) code 8.66 % 10.37 % 7.06 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
383 TLNet (Stereo)
This method uses stereo information.
 code 7.69 % 13.71 % 6.73 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
384 Shift R-CNN (mono) code 6.82 % 11.84 % 5.27 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
385 SparVox3D 6.39 % 10.20 % 5.06 % 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.
386 GS3D 6.08 % 8.41 % 4.94 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
387 MVRA + I-FRCNN+ 5.84 % 9.05 % 4.50 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
388 WeakM3D code 5.66 % 11.82 % 4.08 % 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.
389 ROI-10D 4.91 % 9.78 % 3.74 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
390 3D-GCK 4.57 % 5.79 % 3.64 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
391 FQNet 3.23 % 5.40 % 2.46 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
392 3D-SSMFCNN code 2.63 % 3.20 % 2.40 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
393 VeloFCN
This method makes use of Velodyne laser scans.
 0.14 % 0.02 % 0.21 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
394 EAEPNet 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
395 multi-task CNN 0.00 % 0.00 % 0.00 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
396 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 PiFeNet code 53.92 % 63.25 % 50.53 % 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.
2 CasA++ code 53.84 % 60.14 % 51.35 % 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.
3 TED code 53.48 % 60.13 % 50.89 % 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.
4 UPIDet code 53.32 % 58.91 % 50.82 % 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.
5 ImagePG 52.99 % 60.69 % 50.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 EQ-PVRCNN code 52.81 % 61.73 % 49.87 % 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.
7 WinMamba code 52.59 % 59.75 % 49.13 % 0.1 s 1 core @ 2.5 Ghz (Python)
8 VPFNet code 52.41 % 60.07 % 50.28 % 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.
9 Frustum-PointPillars code 52.23 % 60.98 % 48.30 % 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.
10 LoGoNet code 52.06 % 58.24 % 49.87 % 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.
11 TANet code 51.38 % 60.85 % 47.54 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
12 CasA code 51.37 % 57.95 % 49.08 % 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.
13 2025AAAI-SSLfusion code 51.26 % 58.47 % 49.04 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
14 ... code 51.19 % 58.70 % 48.84 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
15 MLF-DET 50.88 % 56.45 % 47.60 % 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.
16 BVIFusion+ 50.78 % 57.56 % 47.31 % 0.09 s 1 core @ 2.5 Ghz (Python)
17 SVFMamba code 50.68 % 60.47 % 48.07 % N/A s 1 core @ 2.5 Ghz (C/C++)
18 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 50.57 % 59.86 % 46.74 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
19 DPPFA-Net 50.55 % 57.02 % 47.25 % 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.
20 HotSpotNet 50.53 % 57.39 % 46.65 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
21 LumiNet code 50.44 % 57.64 % 46.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
22 VMVS
This method makes use of Velodyne laser scans.
 50.34 % 60.34 % 46.45 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
23 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 50.32 % 58.49 % 46.98 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
24 vsis-PHNet 50.23 % 59.41 % 47.62 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
25 PHNetp 50.23 % 59.41 % 47.62 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
26 CGML 50.20 % 56.57 % 48.12 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
27 3DSSD code 49.94 % 60.54 % 45.73 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
28 PointPainting
This method makes use of Velodyne laser scans.
 49.93 % 58.70 % 46.29 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
29 SemanticVoxels 49.93 % 58.91 % 47.31 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
30 RobusTor3D 49.86 % 56.54 % 47.58 % ... s 1 core @ 2.5 Ghz (C/C++)
31 SFA_IGCL_Focalsconv* code 49.82 % 57.49 % 47.56 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
32 ACDet code 49.82 % 58.35 % 47.17 % 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.
33 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 49.81 % 59.04 % 45.92 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
34 USVLab BSAODet code 49.75 % 56.05 % 47.59 % 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.
35 ACFNet 49.74 % 58.07 % 47.27 % 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.
36 New_VLGCL code 49.68 % 56.96 % 47.38 % 0.4 s 1 core @ 2.5 Ghz (Python)
37 dsvd+vx 49.68 % 58.10 % 47.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 F-PointNet
This method makes use of Velodyne laser scans.
 code 49.57 % 57.13 % 45.48 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
39 VLGCL_NoText code 49.31 % 56.49 % 47.20 % 0.3 s 1 core @ 2.5 Ghz (Python)
40 CEF code 49.16 % 57.58 % 45.27 % 0.03 s 1 core @ 2.5 Ghz (Python)
41 R2Pfusion-Det 49.02 % 56.91 % 45.38 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
42 FocalsConv* 48.98 % 55.61 % 45.63 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
43 F-ConvNet
This method makes use of Velodyne laser scans.
 code 48.96 % 57.04 % 44.33 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
44 HVNet 48.86 % 54.84 % 46.33 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
45 CAT-Det 48.78 % 57.13 % 45.56 % 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.
46 STD code 48.72 % 60.02 % 44.55 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
47 PointPillars
This method makes use of Velodyne laser scans.
 code 48.64 % 57.60 % 45.78 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
48 FIRM-Net_SCF+ 48.61 % 55.81 % 46.25 % 0.07 s 1 core @ 2.5 Ghz (Python)
49 EPNet++ 48.47 % 56.24 % 45.73 % 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.
50 FIRM-Net-SCF 48.47 % 55.70 % 46.08 % 0.07 s 1 core @ 2.5 Ghz (Python)
51 MGAF-3DSSD code 48.46 % 56.09 % 44.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
52 Fast-CLOCs 48.27 % 57.19 % 44.55 % 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 FromVoxelToPoint code 48.15 % 56.54 % 45.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
54 EOTL code 47.80 % 56.52 % 43.36 % 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.
55 HMFI code 47.77 % 55.61 % 45.17 % 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.
56 SpaA 47.67 % 53.74 % 45.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 P2V-RCNN 47.36 % 54.15 % 45.10 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
58 Voxel RCNN-Focal* code 47.35 % 54.62 % 44.42 % 0.2 s 1 core @ 2.5 Ghz (Python)
59 Point-GNN
This method makes use of Velodyne laser scans.
 code 47.07 % 55.36 % 44.61 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
60 3ONet 47.05 % 56.76 % 44.62 % 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.
61 SCNet
This method makes use of Velodyne laser scans.
 46.73 % 56.87 % 42.74 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
62 HMNet 46.69 % 54.73 % 43.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 PASS-PV-RCNN-Plus 46.36 % 51.47 % 44.10 % 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.
64 Voxel RCNN* code 46.18 % 54.45 % 44.01 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
65 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 46.13 % 54.77 % 42.84 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
66 ARPNET 45.92 % 55.48 % 42.54 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
67 WWW 45.86 % 53.53 % 43.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 45.82 % 52.03 % 43.81 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
69 DPFusion code 45.78 % 53.33 % 43.53 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
Y. Mo, Y. Wu, J. Zhao, Y. Hu, J. Wang and J. Yan: Enhancing LiDAR Point Features with Foundation Model Priors for 3D Object Detection. ITSC 2025.
70 SVGA-Net 45.68 % 53.09 % 43.30 % 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.
71 epBRM
This method makes use of Velodyne laser scans.
 code 45.49 % 52.48 % 42.75 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
72 PG-RCNN code 45.48 % 51.63 % 43.30 % 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.
73 PDV code 45.45 % 51.95 % 43.33 % 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.
74 MLOD
This method makes use of Velodyne laser scans.
 code 45.40 % 55.09 % 41.42 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
75 CAIA_PRO code 45.16 % 51.68 % 42.69 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
76 IA-SSD (single) code 45.07 % 52.73 % 42.75 % 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.
77 DFAF3D 45.01 % 52.86 % 42.73 % 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.
78 MPC3DNet 44.89 % 49.33 % 43.01 % 0.05 s GPU @ 1.5 Ghz (Python)
79 SRDL 44.84 % 52.42 % 42.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
80 M3DeTR code 44.78 % 50.63 % 42.57 % 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.
81 SIF 44.28 % 52.05 % 42.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
82 work6_new1 44.24 % 51.79 % 41.96 % 0.5 s GPU @ 2.5 Ghz (Python)
83 NoText_VLGCL code 44.18 % 51.36 % 41.91 % 0.2 s 1 core @ 2.5 Ghz (Python)
84 DVFENet 44.12 % 50.98 % 41.62 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
85 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 43.85 % 52.15 % 41.68 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
86 S-AT GCN 43.43 % 50.63 % 41.58 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
87 XPillars
This method makes use of Velodyne laser scans.
 43.10 % 51.95 % 40.57 % 0.02 s GPU @ 2.5 Ghz (Python)
88 BirdNet+
This method makes use of Velodyne laser scans.
 code 42.87 % 48.90 % 40.59 % 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.
89 L-AUG 42.84 % 50.32 % 40.29 % 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.
90 PCNet3D++ 42.81 % 50.62 % 40.36 % 0.05 s GPU @ 3.5 Ghz (Python)
91 DSFNet 42.68 % 51.31 % 39.65 % 0.05 s GPU @ 2.5 Ghz (Python)
92 IA-SSD (multi) code 42.61 % 51.76 % 40.51 % 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.
93 SA V1 42.45 % 49.18 % 40.32 % 0.5 s GPU @ 2.5 Ghz (Python)
94 XView 42.42 % 47.24 % 39.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
95 CS3D 42.05 % 49.61 % 39.90 % 0.5 s 1 core @ 2.5 Ghz (Python)
96 GraphAlign(ICCV2023) code 41.95 % 46.61 % 40.05 % 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.
97 MSFASA-3DNet 41.94 % 48.10 % 40.05 % 0.03 s GPU @ 2.5 Ghz (Python)
98 M3DNet 41.84 % 50.30 % 39.49 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
99 SeSame-voxel code 41.59 % 50.12 % 37.79 % 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.
100 HINTED code 41.55 % 53.09 % 39.18 % 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.
101 SeSame-point code 41.22 % 48.25 % 39.18 % 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.
102 R_Pillar 40.97 % 48.85 % 38.40 % 0.05 s GPU @ 2.5 Ghz (Python)
103 PFF3D
This method makes use of Velodyne laser scans.
 code 40.94 % 48.74 % 38.54 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
104 geo-pillars 40.91 % 47.95 % 38.49 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
105 DensePointPillars 40.31 % 47.80 % 37.49 % 0.03 s GPU @ 2.5 Ghz (Python)
106 Ped_Net 40.27 % 48.22 % 37.90 % 0.5 s GPU @ 2.5 Ghz (Python + C/C++)
107 Fade-kd 39.38 % 47.19 % 36.42 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
108 DSGN++
This method uses stereo information.
 code 38.92 % 50.26 % 35.12 % 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.
109 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 38.79 % 47.51 % 35.85 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
110 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 38.28 % 45.53 % 35.37 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
111 PointPillars_mmdet3d 38.07 % 45.74 % 35.76 % 0.03 s 1 core @ 2.5 Ghz (Python)
112 PL++: PV-RCNN++
This method uses stereo information.
This method makes use of Velodyne laser scans.
 37.96 % 44.88 % 35.48 % 0.342 s RTX 4060Ti (Python)
X. Gong, X. Huang, S. Chen and B. Zhang: Enhancing 3D Detection Accuracy in Autonomous Driving through Pseudo-LiDAR Augmentation and Downsampling. 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) 2024.
113 CSW3D
This method makes use of Velodyne laser scans.
 37.96 % 49.27 % 33.83 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
114 StereoDistill 37.75 % 50.79 % 34.28 % 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.
115 SeSame-pillar code 37.31 % 44.21 % 35.17 % 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 T-SSD 35.17 % 43.33 % 33.02 % 0.04 1 core @ 2.0 Ghz (C/C++)
117 DMF
This method uses stereo information.
 34.92 % 42.08 % 32.69 % 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.
118 SparsePool code 34.15 % 43.33 % 31.78 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
119 MMLAB LIGA-Stereo
This method uses stereo information.
 code 34.13 % 44.71 % 30.42 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
120 SeSame-voxel w/score code 33.76 % 39.42 % 31.31 % 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.
121 AVOD
This method makes use of Velodyne laser scans.
 code 33.57 % 42.58 % 30.14 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
122 SparsePool code 33.22 % 41.55 % 29.66 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
123 SeSame-pillar w/scor code 32.78 % 39.11 % 30.87 % 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.
124 CG-Stereo
This method uses stereo information.
 29.56 % 39.24 % 25.87 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
125 PointRGBNet 29.32 % 38.07 % 26.94 % 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.
126 Fade 3D code 29.18 % 36.14 % 27.42 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
W. Ye, Q. Xia, H. Wu, Z. Dong, R. Zhong, C. Wang and C. Wen: Fade3D: Fast and Deployable 3D Object Detection for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems 2025.
127 Disp R-CNN
This method uses stereo information.
 code 29.12 % 42.72 % 25.09 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
128 Disp R-CNN (velo)
This method uses stereo information.
 code 28.34 % 40.21 % 24.46 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
129 SeSame-point w/score code 25.79 % 33.98 % 22.50 % 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.
130 DDStereo
This method uses stereo information.
 24.72 % 35.80 % 22.06 % 0.02 s GPU @ 2.5 Ghz (Python)
131 CPD++(unsupervised) code 24.43 % 28.36 % 23.22 % 0.1 s GPU @ >3.5 Ghz (Python)
132 BirdNet
This method makes use of Velodyne laser scans.
 23.06 % 28.20 % 21.65 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
133 OC Stereo
This method uses stereo information.
 code 20.80 % 29.79 % 18.62 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
134 YOLOStereo3D
This method uses stereo information.
 code 20.76 % 31.01 % 18.41 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
135 DSGN
This method uses stereo information.
 code 20.75 % 26.61 % 18.86 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
136 Complexer-YOLO
This method makes use of Velodyne laser scans.
 18.26 % 21.42 % 17.06 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
137 TopNet-Retina
This method makes use of Velodyne laser scans.
 14.57 % 18.04 % 12.48 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
138 MonOri code 14.35 % 20.86 % 12.45 % 0.03 s 4 cores @ 2.5 Ghz (Python)
H. Yao, P. Han, J. Chen, Z. Wang, Y. Qiu, X. Wang, Y. wang, X. Chai, C. Cao and W. Jin: MonOri: Orientation-Guided PnP for Monocular 3-D Object Detection. IEEE Transactions on Neural Networks and Learning Systems 2025.
139 RT3D-GMP
This method uses stereo information.
 14.22 % 19.92 % 12.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
140 TopNet-HighRes
This method makes use of Velodyne laser scans.
 13.50 % 19.43 % 11.93 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
141 ESGN
This method uses stereo information.
 13.03 % 17.94 % 11.54 % 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.
142 MonoMH code 12.70 % 18.63 % 11.06 % 0.04 s 1 core @ 2.5 Ghz (Python)
143 MDD-M3D-X 12.53 % 19.10 % 10.78 % 0.01 s 1 core @ 2.5 Ghz (Python)
144 DD3D code 12.51 % 18.58 % 10.65 % 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) .
145 MonoLSS 12.34 % 18.40 % 10.54 % 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.
146 MonoAFKD 12.30 % 18.37 % 10.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
147 PS-fld code 12.23 % 19.03 % 10.53 % 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.
148 CIE 11.94 % 17.90 % 10.34 % 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.
149 MonoHPE-Mask 11.91 % 17.30 % 10.09 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
150 AM 11.87 % 17.55 % 10.21 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
151 MonoCoP 11.80 % 17.69 % 10.22 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
152 MonoHPE 11.70 % 17.57 % 10.12 % 0.04 s 1 core @ 2.5 Ghz (Python)
153 MonoCLUE_all 11.21 % 17.67 % 9.35 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
154 MonoGeo code 11.12 % 17.99 % 9.49 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
155 CPD(unsupervised) code 11.09 % 13.48 % 10.19 % 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.
156 fdaa11 11.09 % 16.97 % 9.35 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
157 OPA-3D code 11.01 % 17.14 % 9.94 % 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.
158 MonoUNI code 10.90 % 16.54 % 9.17 % 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.
159 MonoCLUE 10.81 % 16.60 % 9.12 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
160 MonoLSPF 10.79 % 16.46 % 9.67 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
161 IDEAL-M3D 60% 10.65 % 16.74 % 8.83 % 0.04 s 1 core @ 2.5 Ghz (Python)
162 MonoDTR 10.59 % 16.66 % 9.00 % 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.
163 MonoFRD 10.38 % 15.68 % 8.79 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
Z. Gong, Y. Zhao, F. Zhang, G. Gui, B. Chen, L. Yu, H. Wang, C. Yang and W. Gui: Color intuitive feature guided depth-height fusion and volume rendering for monocular 3D object detection. IEEE Transactions on Intelligent Vehicles(Major Revison) 2024.
164 GUPNet code 10.37 % 15.62 % 8.79 % 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.
165 CMKD code 10.28 % 16.03 % 8.85 % 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.
166 DEVIANT code 9.77 % 14.49 % 8.28 % 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.
167 PS-SVDM 9.75 % 15.03 % 8.37 % 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.
168 MonoNeRD code 9.66 % 15.27 % 8.28 % 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.
169 CaDDN code 9.41 % 14.72 % 8.17 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
170 SGM3D code 9.39 % 15.39 % 8.61 % 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.
171 AMNet+DDAD15M code 9.30 % 14.10 % 8.02 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
H. Pan, Y. Jia, J. Wang and W. Sun: MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods. IEEE Transactions on Intelligent Transportation Systems 2025.
172 AMNet code 9.30 % 14.02 % 7.93 % 0.03 s GPU @ 1.0 Ghz (Python)
H. Pan, Y. Jia, J. Wang and W. Sun: MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods. IEEE Transactions on Intelligent Transportation Systems 2025.
173 MonoRCNN++ code 9.04 % 13.45 % 7.74 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
174 HomoLoss(monoflex) code 8.81 % 13.26 % 7.41 % 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.
175 mdab 8.79 % 13.79 % 7.99 % 0.02 s 1 core @ 2.5 Ghz (Python)
176 temp 8.46 % 12.62 % 7.24 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
177 MonoDDE 8.41 % 12.38 % 7.16 % 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.
178 Mix-Teaching code 8.40 % 12.34 % 7.06 % 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.
179 MDSNet 8.18 % 12.05 % 7.03 % 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.
180 PS-SVDM 8.11 % 12.70 % 6.84 % 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.
181 LPCG-Monoflex code 7.92 % 12.11 % 6.61 % 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.
182 RefinedMPL 7.92 % 13.09 % 7.25 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
183 Cube R-CNN code 7.65 % 11.67 % 6.60 % 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.
184 MonoRUn code 7.59 % 11.70 % 6.34 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
185 MonoFlex 7.36 % 10.36 % 6.29 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
186 DA3D+KM3D+v2-99 code 7.06 % 10.32 % 6.10 % 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.
187 MonoPair 7.04 % 10.99 % 6.29 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
188 monodle code 6.96 % 10.73 % 6.20 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
189 MonOAPC 6.82 % 9.62 % 5.78 % 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.
190 UniCuboid 6.75 % 10.26 % 5.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
191 TopNet-DecayRate
This method makes use of Velodyne laser scans.
 6.59 % 8.78 % 6.25 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
192 Shift R-CNN (mono) code 5.66 % 8.58 % 4.49 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
193 FMF-occlusion-net 5.62 % 8.69 % 5.25 % 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.
194 Aug3D-RPN 5.22 % 7.14 % 4.21 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
195 TopNet-UncEst
This method makes use of Velodyne laser scans.
 4.60 % 6.88 % 3.79 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
196 MonoPSR code 4.56 % 7.24 % 4.11 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
197 DFR-Net 4.52 % 6.66 % 3.71 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
198 monospb 4.50 % 6.15 % 3.76 % 0.01 s 1 core @ 2.5 Ghz (Python)
199 QD-3DT
This is an online method (no batch processing).
 code 4.23 % 6.62 % 3.39 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
200 DA3D+KM3D code 4.05 % 5.94 % 3.55 % 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.
201 M3D-RPN code 4.05 % 5.65 % 3.29 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
202 DDMP-3D 4.02 % 5.53 % 3.36 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
203 CMAN 3.96 % 5.24 % 3.18 % 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.
204 D4LCN code 3.86 % 5.06 % 3.59 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
205 RT3DStereo
This method uses stereo information.
 3.65 % 4.72 % 3.00 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
206 DA3D code 3.27 % 4.93 % 2.74 % 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.
207 MonoEF 3.05 % 4.61 % 2.85 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
208 MonoLiG code 2.72 % 3.74 % 2.55 % 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.
209 SS3D 2.09 % 2.48 % 1.61 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
210 SparVox3D 2.05 % 2.90 % 1.69 % 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.
211 PGD-FCOS3D code 1.88 % 2.82 % 1.54 % 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.
212 Plane-Constraints code 1.16 % 1.87 % 1.13 % 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.
213 GATE3D code 0.17 % 0.15 % 0.17 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
214 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 ImagePG 78.92 % 89.97 % 71.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 UPIDet code 78.19 % 89.65 % 71.13 % 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.
3 CasA++ code 76.99 % 88.93 % 70.10 % 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 TED code 76.95 % 89.54 % 70.31 % 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.
5 WinMamba code 76.92 % 88.86 % 69.85 % 0.1 s 1 core @ 2.5 Ghz (Python)
6 CasA code 75.74 % 88.99 % 68.47 % 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 LoGoNet code 74.92 % 85.85 % 67.62 % 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.
8 MLF-DET 74.88 % 86.20 % 66.75 % 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.
9 USVLab BSAODet code 74.38 % 85.01 % 67.38 % 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.
10 SpaA 74.34 % 87.14 % 66.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 vsis-PHNet 74.17 % 88.70 % 67.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
12 HMFI code 74.06 % 85.69 % 67.11 % 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.
13 EQ-PVRCNN code 73.30 % 86.25 % 65.49 % 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.
14 PHNetc 72.91 % 86.97 % 66.23 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
15 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 72.61 % 83.93 % 65.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
16 CAT-Det 72.51 % 85.35 % 65.55 % 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 WWW 72.08 % 83.76 % 65.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
18 CGML 72.05 % 83.52 % 66.71 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
19 MPC3DNet 71.88 % 84.37 % 64.96 % 0.05 s GPU @ 1.5 Ghz (Python)
20 dsvd+vx 71.87 % 88.92 % 63.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
21 VLGCL_NoText code 71.83 % 83.04 % 66.48 % 0.3 s 1 core @ 2.5 Ghz (Python)
22 BtcDet
This method makes use of Velodyne laser scans.
 code 71.76 % 84.48 % 64.70 % 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.
23 ACFNet 71.68 % 85.76 % 65.33 % 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.
24 RagNet3D code 71.64 % 85.10 % 65.02 % 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 .
25 PointPainting
This method makes use of Velodyne laser scans.
 71.54 % 83.91 % 62.97 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
26 PASS-PV-RCNN-Plus 71.51 % 83.03 % 63.85 % 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.
27 RangeIoUDet
This method makes use of Velodyne laser scans.
 71.49 % 85.99 % 63.62 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
28 ACDet code 71.48 % 87.76 % 64.69 % 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.
29 IA-SSD (single) code 71.44 % 85.91 % 63.41 % 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.
30 PDV code 71.31 % 85.54 % 64.40 % 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 3ONet 71.29 % 85.17 % 62.99 % 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.
32 DFAF3D 71.27 % 85.75 % 64.25 % 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.
33 HVNet 71.17 % 83.97 % 63.65 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
34 M3DeTR code 70.89 % 85.03 % 63.14 % 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.
35 PG-RCNN code 70.65 % 84.94 % 64.03 % 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.
36 DPFusion code 70.26 % 83.57 % 62.01 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
Y. Mo, Y. Wu, J. Zhao, Y. Hu, J. Wang and J. Yan: Enhancing LiDAR Point Features with Foundation Model Priors for 3D Object Detection. ITSC 2025.
37 SPG_mini
This method makes use of Velodyne laser scans.
 code 70.09 % 82.66 % 63.61 % 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 GraphAlign(ICCV2023) code 69.43 % 80.71 % 63.57 % 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.
39 FIRM-Net_SCF+ 69.29 % 83.14 % 61.31 % 0.07 s 1 core @ 2.5 Ghz (Python)
40 BVIFusion+ 69.18 % 83.51 % 62.71 % 0.09 s 1 core @ 2.5 Ghz (Python)
41 FIRM-Net-SCF 69.11 % 83.08 % 61.21 % 0.07 s 1 core @ 2.5 Ghz (Python)
42 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 68.89 % 82.49 % 62.41 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
43 F-ConvNet
This method makes use of Velodyne laser scans.
 code 68.88 % 84.16 % 60.05 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
44 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 68.73 % 83.43 % 61.85 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
45 New_VLGCL code 68.66 % 78.63 % 64.57 % 0.4 s 1 core @ 2.5 Ghz (Python)
46 CEF code 68.52 % 85.70 % 61.76 % 0.03 s 1 core @ 2.5 Ghz (Python)
47 HotSpotNet 68.51 % 83.29 % 61.84 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
48 LumiNet code 68.42 % 85.56 % 61.65 % 0.1 s 1 core @ 2.5 Ghz (Python)
49 RobusTor3D 68.12 % 79.62 % 62.50 % ... s 1 core @ 2.5 Ghz (C/C++)
50 P2V-RCNN 68.06 % 81.09 % 60.73 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
51 SVFMamba code 68.02 % 80.22 % 61.54 % N/A s 1 core @ 2.5 Ghz (C/C++)
52 SFA_IGCL_Focalsconv* code 67.90 % 77.55 % 63.36 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
53 H^23D R-CNN code 67.90 % 82.76 % 60.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
54 CAIA_PRO code 67.77 % 82.00 % 60.92 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
55 FocalsConv* 67.73 % 80.18 % 62.82 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
56 VPFNet code 67.66 % 80.83 % 61.36 % 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.
57 3DSSD code 67.62 % 85.04 % 61.14 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
58 Fast-CLOCs 67.55 % 83.34 % 59.61 % 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.
59 DVFENet 67.40 % 82.29 % 60.71 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
60 FromVoxelToPoint code 67.36 % 82.68 % 59.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
61 Point-GNN
This method makes use of Velodyne laser scans.
 code 67.28 % 81.17 % 59.67 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
62 HINTED code 67.27 % 81.53 % 60.88 % 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.
63 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 67.24 % 82.56 % 60.28 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
64 STD code 67.23 % 81.36 % 59.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
65 Voxel RCNN* code 67.19 % 84.13 % 60.87 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
66 2025AAAI-SSLfusion code 67.10 % 79.00 % 61.03 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
67 XPillars
This method makes use of Velodyne laser scans.
 66.92 % 79.31 % 61.11 % 0.02 s GPU @ 2.5 Ghz (Python)
68 ... code 66.87 % 78.82 % 61.62 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
69 HMNet 66.86 % 83.17 % 60.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 SVGA-Net 66.82 % 81.25 % 59.37 % 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.
71 S-AT GCN 66.71 % 78.53 % 60.19 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
72 R2Pfusion-Det 66.53 % 81.74 % 57.90 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
73 ARPNET 66.39 % 82.32 % 58.80 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
74 IA-SSD (multi) code 66.29 % 81.30 % 59.58 % 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.
75 MGAF-3DSSD code 66.00 % 83.03 % 57.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
76 Voxel RCNN-Focal* code 65.96 % 76.61 % 61.13 % 0.2 s 1 core @ 2.5 Ghz (Python)
77 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 65.85 % 80.00 % 58.69 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
78 EOTL code 65.76 % 81.44 % 56.47 % 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.
79 SA V1 65.54 % 78.53 % 58.69 % 0.5 s GPU @ 2.5 Ghz (Python)
80 T-SSD 65.13 % 82.12 % 59.42 % 0.04 1 core @ 2.0 Ghz (C/C++)
81 MSFASA-3DNet 65.12 % 79.61 % 58.84 % 0.03 s GPU @ 2.5 Ghz (Python)
82 NoText_VLGCL code 64.76 % 79.14 % 60.05 % 0.2 s 1 core @ 2.5 Ghz (Python)
83 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 64.54 % 79.65 % 57.84 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
84 SRDL 64.52 % 79.64 % 57.90 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
85 PCNet3D++ 64.20 % 80.19 % 57.09 % 0.05 s GPU @ 3.5 Ghz (Python)
86 SIF 64.13 % 79.32 % 57.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
87 DSFNet 63.85 % 76.70 % 56.67 % 0.05 s GPU @ 2.5 Ghz (Python)
88 TANet code 63.77 % 79.16 % 56.21 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
89 geo-pillars 63.75 % 77.99 % 56.39 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
90 DensePointPillars 63.27 % 75.14 % 56.70 % 0.03 s GPU @ 2.5 Ghz (Python)
91 XView 63.06 % 81.32 % 56.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
92 EPNet++ 62.94 % 78.57 % 56.62 % 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.
93 PointPillars
This method makes use of Velodyne laser scans.
 code 62.73 % 79.90 % 55.58 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
94 L-AUG 62.56 % 75.41 % 56.86 % 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.
95 CS3D 62.47 % 78.77 % 55.59 % 0.5 s 1 core @ 2.5 Ghz (Python)
96 work6_new1 62.26 % 76.21 % 55.99 % 0.5 s GPU @ 2.5 Ghz (Python)
97 Fade-kd 61.78 % 77.70 % 55.39 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
98 PointPillars_mmdet3d 61.70 % 75.76 % 54.77 % 0.03 s 1 core @ 2.5 Ghz (Python)
99 SeSame-point code 61.70 % 75.73 % 55.27 % 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.
100 F-PointNet
This method makes use of Velodyne laser scans.
 code 61.37 % 77.26 % 53.78 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
101 M3DNet 61.04 % 75.48 % 54.48 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
102 R_Pillar 60.30 % 75.76 % 53.12 % 0.05 s GPU @ 2.5 Ghz (Python)
103 SeSame-pillar code 60.21 % 72.22 % 53.67 % 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.
104 epBRM
This method makes use of Velodyne laser scans.
 code 59.79 % 75.13 % 53.36 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
105 BirdNet+
This method makes use of Velodyne laser scans.
 code 59.58 % 70.84 % 54.20 % 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.
106 SeSame-voxel code 59.36 % 76.95 % 53.14 % 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.
107 DMF
This method uses stereo information.
 57.99 % 71.92 % 51.55 % 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.
108 PointRGBNet 57.59 % 73.09 % 51.78 % 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.
109 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 57.12 % 69.39 % 51.09 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
110 PiFeNet code 56.94 % 72.80 % 50.04 % 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.
111 SCNet
This method makes use of Velodyne laser scans.
 56.39 % 73.73 % 49.99 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
112 PFF3D
This method makes use of Velodyne laser scans.
 code 55.71 % 72.67 % 49.58 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
113 MLOD
This method makes use of Velodyne laser scans.
 code 55.06 % 73.03 % 48.21 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
114 Fade 3D code 55.00 % 71.51 % 48.65 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
W. Ye, Q. Xia, H. Wu, Z. Dong, R. Zhong, C. Wang and C. Wen: Fade3D: Fast and Deployable 3D Object Detection for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems 2025.
115 PL++: PV-RCNN++
This method uses stereo information.
This method makes use of Velodyne laser scans.
 53.64 % 67.88 % 46.87 % 0.342 s RTX 4060Ti (Python)
X. Gong, X. Huang, S. Chen and B. Zhang: Enhancing 3D Detection Accuracy in Autonomous Driving through Pseudo-LiDAR Augmentation and Downsampling. 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) 2024.
116 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 52.15 % 72.45 % 46.57 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
117 DSGN++
This method uses stereo information.
 code 49.37 % 68.29 % 43.79 % 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.
118 StereoDistill 48.37 % 69.46 % 42.69 % 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.
119 AVOD
This method makes use of Velodyne laser scans.
 code 48.15 % 64.11 % 42.37 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
120 SeSame-voxel w/score code 45.61 % 58.94 % 40.68 % 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.
121 BirdNet
This method makes use of Velodyne laser scans.
 41.56 % 58.64 % 36.94 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
122 SparsePool code 40.74 % 56.52 % 36.68 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
123 MMLAB LIGA-Stereo
This method uses stereo information.
 code 40.60 % 58.95 % 35.27 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
124 CPD++(unsupervised) code 36.97 % 56.35 % 32.33 % 0.1 s GPU @ >3.5 Ghz (Python)
125 TopNet-Retina
This method makes use of Velodyne laser scans.
 36.83 % 47.48 % 33.58 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
126 CG-Stereo
This method uses stereo information.
 36.25 % 55.33 % 32.17 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
127 SparsePool code 35.24 % 43.55 % 30.15 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
128 Disp R-CNN (velo)
This method uses stereo information.
 code 27.04 % 44.19 % 23.58 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
129 Disp R-CNN
This method uses stereo information.
 code 27.04 % 44.19 % 23.58 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
130 Complexer-YOLO
This method makes use of Velodyne laser scans.
 25.43 % 32.00 % 22.88 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
131 DDStereo
This method uses stereo information.
 24.36 % 37.95 % 20.87 % 0.02 s GPU @ 2.5 Ghz (Python)
132 DSGN
This method uses stereo information.
 code 21.04 % 31.23 % 18.93 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
133 SeSame-pillar w/scor code 19.53 % 15.92 % 17.61 % 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.
134 OC Stereo
This method uses stereo information.
 code 19.23 % 32.47 % 17.11 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
135 TopNet-DecayRate
This method makes use of Velodyne laser scans.
 16.00 % 23.02 % 13.24 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
136 RT3D-GMP
This method uses stereo information.
 13.92 % 20.59 % 12.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
137 TopNet-UncEst
This method makes use of Velodyne laser scans.
 9.18 % 12.31 % 8.14 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
138 ESGN
This method uses stereo information.
 9.02 % 15.78 % 7.96 % 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.
139 SeSame-point w/score code 8.90 % 10.65 % 7.68 % 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.
140 CMKD code 8.15 % 14.66 % 7.23 % 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.
141 MonOri code 7.86 % 12.34 % 7.05 % 0.03 s 4 cores @ 2.5 Ghz (Python)
H. Yao, P. Han, J. Chen, Z. Wang, Y. Qiu, X. Wang, Y. wang, X. Chai, C. Cao and W. Jin: MonOri: Orientation-Guided PnP for Monocular 3-D Object Detection. IEEE Transactions on Neural Networks and Learning Systems 2025.
142 PS-fld code 7.29 % 12.80 % 6.05 % 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.
143 MDD-M3D-X 7.15 % 12.20 % 6.14 % 0.01 s 1 core @ 2.5 Ghz (Python)
144 MonoLiG code 6.49 % 9.48 % 5.46 % 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.
145 TopNet-HighRes
This method makes use of Velodyne laser scans.
 6.48 % 9.99 % 6.76 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
146 CPD(unsupervised) code 6.27 % 9.11 % 5.34 % 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.
147 MonoLSPF 6.05 % 8.99 % 5.33 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
148 MonoMH code 5.92 % 9.21 % 5.30 % 0.04 s 1 core @ 2.5 Ghz (Python)
149 DA3D+KM3D+v2-99 code 5.82 % 9.73 % 4.88 % 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.
150 MonoPSR code 5.78 % 9.87 % 4.57 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
151 DD3D code 5.69 % 9.20 % 5.20 % 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) .
152 MonoLSS 5.52 % 8.88 % 4.98 % 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.
153 MonoAFKD 5.44 % 8.75 % 4.88 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
154 MonoGeo code 5.43 % 8.76 % 4.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
155 fdaa11 5.43 % 8.89 % 4.52 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
156 IDEAL-M3D 60% 5.39 % 8.85 % 4.87 % 0.04 s 1 core @ 2.5 Ghz (Python)
157 CaDDN code 5.38 % 9.67 % 4.75 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
158 Mix-Teaching code 5.36 % 8.56 % 4.62 % 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.
159 PS-SVDM 5.34 % 9.20 % 4.31 % 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.
160 MonoCoP 5.27 % 8.31 % 4.44 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
161 MonoUNI code 5.03 % 8.25 % 4.50 % 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.
162 MonoHPE-Mask 4.99 % 8.69 % 4.75 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
163 LPCG-Monoflex code 4.90 % 8.14 % 3.86 % 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.
164 MonoHPE 4.82 % 7.58 % 4.78 % 0.04 s 1 core @ 2.5 Ghz (Python)
165 Plane-Constraints code 4.79 % 8.67 % 3.90 % 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.
166 AMNet code 4.62 % 7.89 % 4.00 % 0.03 s GPU @ 1.0 Ghz (Python)
H. Pan, Y. Jia, J. Wang and W. Sun: MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods. IEEE Transactions on Intelligent Transportation Systems 2025.
167 MonoFRD 4.55 % 8.44 % 4.14 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
Z. Gong, Y. Zhao, F. Zhang, G. Gui, B. Chen, L. Yu, H. Wang, C. Yang and W. Gui: Color intuitive feature guided depth-height fusion and volume rendering for monocular 3D object detection. IEEE Transactions on Intelligent Vehicles(Major Revison) 2024.
168 MonoCLUE 4.43 % 7.15 % 4.15 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
169 MonoDDE 4.36 % 6.68 % 3.76 % 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.
170 AM 4.18 % 6.02 % 3.62 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
171 MonoDTR 4.11 % 5.84 % 3.48 % 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.
172 RT3DStereo
This method uses stereo information.
 4.10 % 7.03 % 3.88 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
173 HomoLoss(monoflex) code 4.09 % 6.81 % 3.78 % 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.
174 MonoCLUE_all 4.04 % 7.48 % 3.43 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
175 DFR-Net 4.00 % 5.99 % 3.95 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
176 DEVIANT code 3.97 % 6.42 % 3.51 % 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 GUPNet code 3.85 % 6.94 % 3.64 % 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.
178 OPA-3D code 3.75 % 6.01 % 3.56 % 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.
179 CIE 3.74 % 6.13 % 3.18 % 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.
180 PS-SVDM 3.64 % 6.84 % 3.04 % 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.
181 SGM3D code 3.63 % 7.05 % 3.33 % 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.
182 AMNet+DDAD15M code 3.61 % 5.54 % 3.19 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
H. Pan, Y. Jia, J. Wang and W. Sun: MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods. IEEE Transactions on Intelligent Transportation Systems 2025.
183 UniCuboid 3.60 % 6.73 % 3.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
184 Cube R-CNN code 3.35 % 5.01 % 3.23 % 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.
185 Aug3D-RPN 3.33 % 5.44 % 2.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
186 MonOAPC 3.31 % 6.54 % 3.05 % 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.
187 temp 3.31 % 5.62 % 2.71 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
188 monodle code 3.28 % 5.34 % 2.83 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
189 MDSNet 3.22 % 5.99 % 2.62 % 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.
190 DDMP-3D 3.14 % 4.92 % 2.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
191 QD-3DT
This is an online method (no batch processing).
 code 3.02 % 5.71 % 2.73 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
192 MonoPair 2.87 % 4.76 % 2.42 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
193 MonoNeRD code 2.80 % 5.24 % 2.55 % 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.
194 MonoFlex 2.67 % 4.41 % 2.50 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
195 mdab 2.63 % 4.95 % 2.65 % 0.02 s 1 core @ 2.5 Ghz (Python)
196 RefinedMPL 2.42 % 4.23 % 2.14 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
197 MonoRCNN++ code 2.31 % 3.50 % 2.01 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
198 SS3D 1.89 % 3.45 % 1.44 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
199 DA3D code 1.89 % 3.46 % 1.51 % 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.
200 D4LCN code 1.82 % 2.72 % 1.79 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
201 PGD-FCOS3D code 1.79 % 3.54 % 1.56 % 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.
202 monospb 1.76 % 3.24 % 1.81 % 0.01 s 1 core @ 2.5 Ghz (Python)
203 FMF-occlusion-net 1.65 % 1.91 % 1.75 % 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 CMAN 1.48 % 1.76 % 1.17 % 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.
205 DA3D+KM3D code 1.44 % 2.88 % 1.37 % 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.
206 MonoEF 1.18 % 2.36 % 1.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
207 M3D-RPN code 0.81 % 1.25 % 0.78 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
208 MonoRUn code 0.73 % 1.14 % 0.66 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
209 Shift R-CNN (mono) code 0.38 % 0.76 % 0.41 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
210 GATE3D code 0.00 % 0.00 % 0.00 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
211 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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