3D Object Detection Evaluation 2017


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

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

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 VirConv-S code 87.20 % 92.48 % 82.45 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
2 UDeerPEP code 86.72 % 91.77 % 82.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Z. Dong, H. Ji, X. Huang, W. Zhang, X. Zhan and J. Chen: PeP: a Point enhanced Painting method for unified point cloud tasks. 2023.
3 VirConv-T code 86.25 % 92.54 % 81.24 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
4 ViKIENet-R 86.04 % 91.20 % 81.18 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Z. Yu, B. Qiu and A. Khong: ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network. CVPR 2025.
5 LongSF code 85.66 % 92.28 % 80.84 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
P. Gao: LongSF: Long State Fusion with SSMs for Multimodal 3D Object Detection. 2025.
6 kfc 85.59 % 92.52 % 80.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 MPCF code 85.50 % 92.46 % 80.69 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
P. Gao and P. Zhang: MPCF: Multi-Phase Consolidated Fusion for Multi-Modal 3D Object Detection with Pseudo Point Cloud. 2024.
8 TSSTDet 85.47 % 91.84 % 80.65 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang, D. Bui and M. Yoo: TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer. IEEE Sensors Journal 2024.
9 3ONet 85.47 % 92.03 % 78.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
10 TED code 85.28 % 91.61 % 80.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
11 MVFusion code 85.20 % 91.65 % 78.28 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
12 SQD++ 85.14 % 92.12 % 80.14 % 0.08 s GPU @ >3.5 Ghz (Python)
13 None 85.14 % 92.12 % 80.14 % 0.05 1 core @ 2.5 Ghz (C/C++)
14 ICD-PSOC 85.07 % 91.66 % 80.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
15 LoGoNet code 85.06 % 91.80 % 80.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
16 TRTConv-L 85.04 % 91.90 % 80.38 % 0.01 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
17 ViKIENet 84.96 % 91.79 % 80.20 % 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.
18 3D-AWARE 84.85 % 91.38 % 80.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
19 P3GMF 84.82 % 90.99 % 81.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
20 SFD code 84.76 % 91.73 % 77.92 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
21 ACFNet 84.67 % 90.80 % 80.14 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
22 MS-R code 84.45 % 90.49 % 79.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
23 TRTConv-T 84.40 % 91.38 % 80.07 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
24 ICD-PSI 84.35 % 90.55 % 79.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 3D HANet code 84.18 % 90.79 % 77.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, Y. Chen, G. Cai, G. Chen, D. Xie, J. Su and Z. Wang: 3D HANet: A Flexible 3D Heatmap Auxiliary Network for Object Detection. IEEE Transactions on Geoscience and Remote Sensing 2023.
26 CasA++ code 84.04 % 90.68 % 79.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
27 L-AUG 83.84 % 90.53 % 79.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
28 auto_test 83.58 % 92.33 % 80.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
29 LumiNet 83.32 % 91.76 % 78.29 % 0.1 s 1 core @ 2.5 Ghz (Python)
30 GraR-VoI code 83.27 % 91.89 % 77.78 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
31 GLENet-VR code 83.23 % 91.67 % 78.43 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D object detectors with generative label uncertainty estimation. International Journal of Computer Vision 2023.
Y. Zhang, J. Hou and Y. Yuan: A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks. International Journal of Computer Vision 2023.
32 VPFNet code 83.21 % 91.02 % 78.20 % 0.06 s 2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. IEEE Transactions on Multimedia 2022.
33 GraR-Po code 83.18 % 91.79 % 77.98 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
34 CasA code 83.06 % 91.58 % 80.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
35 WinMamba code 82.97 % 89.72 % 79.79 % 0.1 s 1 core @ 2.5 Ghz (Python)
36 UPIDet code 82.97 % 89.13 % 80.05 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
37 MLF-DET 82.89 % 91.18 % 77.89 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
38 BtcDet
This method makes use of Velodyne laser scans.
 code 82.86 % 90.64 % 78.09 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
39 GraR-Vo code 82.77 % 91.29 % 77.20 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
40 SPG_mini
This method makes use of Velodyne laser scans.
 code 82.66 % 90.64 % 77.91 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
41 OcTr 82.64 % 90.88 % 77.77 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
C. Zhou, Y. Zhang, J. Chen and D. Huang: OcTr: Octree-based Transformer for 3D Object Detection. CVPR 2023.
42 PA3DNet 82.57 % 90.49 % 77.88 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
M. Wang, L. Zhao and Y. Yue: PA3DNet: 3-D Vehicle Detection with Pseudo Shape Segmentation and Adaptive Camera- LiDAR Fusion. IEEE Transactions on Industrial Informatics 2023.
43 SE-SSD
This method makes use of Velodyne laser scans.
 code 82.54 % 91.49 % 77.15 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
44 ImagePG code 82.46 % 91.00 % 79.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 DVF-V 82.45 % 89.40 % 77.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
46 GraR-Pi code 82.42 % 90.94 % 77.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
47 DVF-PV 82.40 % 90.99 % 77.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
48 3D Dual-Fusion code 82.40 % 91.01 % 79.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
Y. Kim, K. Park, M. Kim, D. Kum and J. Choi: 3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection. arXiv preprint arXiv:2211.13529 2022.
49 DPFusion code 82.35 % 90.98 % 77.26 % 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.
50 RDIoU code 82.30 % 90.65 % 77.26 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single- stage 3D Object Detection. ECCV 2022.
51 PVT-SSD 82.29 % 90.65 % 76.85 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He and W. Ouyang: PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer. CVPR 2023.
52 Focals Conv code 82.28 % 90.55 % 77.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
53 CLOCs code 82.28 % 89.16 % 77.23 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
54 GraphAlign(ICCV2023) code 82.23 % 90.90 % 79.67 % 0.03 s GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
55 SASA
This method makes use of Velodyne laser scans.
 code 82.16 % 88.76 % 77.16 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
56 PG-RCNN code 82.13 % 89.38 % 77.33 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
57 SPG
This method makes use of Velodyne laser scans.
 code 82.13 % 90.50 % 78.90 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
58 RobusTor3D 82.12 % 90.78 % 79.36 % ... s 1 core @ 2.5 Ghz (C/C++)
59 VoTr-TSD code 82.09 % 89.90 % 79.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
60 Pyramid R-CNN 82.08 % 88.39 % 77.49 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
61 VoxSeT code 82.06 % 88.53 % 77.46 % 33 ms 1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
62 EQ-PVRCNN code 82.01 % 90.13 % 77.53 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
63 EPNet++ 81.96 % 91.37 % 76.71 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
64 USVLab BSAODet code 81.95 % 88.66 % 77.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
65 MoAd code 81.94 % 88.60 % 77.15 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
66 HMFI code 81.93 % 88.90 % 77.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
67 CEF code 81.93 % 88.19 % 77.14 % 0.03 s 1 core @ 2.5 Ghz (Python)
68 RagNet3D code 81.91 % 88.74 % 77.45 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Chen, Y. Han, Z. Yan, J. Qian, J. Li and J. Yang: Ragnet3d: Learning Distinguishable Representation for Pooled Grids in 3d Object Detection. Available at SSRN 4979473 .
69 PDV code 81.86 % 90.43 % 77.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
70 LGFE 81.83 % 88.63 % 75.10 % 0.03 s 1 core @ 2.5 Ghz (Python)
71 SQD code 81.82 % 91.58 % 79.07 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Mo, Y. Wu, J. Zhao, Z. Hou, W. Huang, Y. Hu, J. Wang and J. Yan: Sparse Query Dense: Enhancing 3D Object Detection with Pseudo Points. ACM MM Oral 2024.
72 CityBrainLab-CT3D code 81.77 % 87.83 % 77.16 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
73 M3DeTR code 81.73 % 90.28 % 76.96 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
74 SIENet code 81.71 % 88.22 % 77.22 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
75 Voxel R-CNN code 81.62 % 90.90 % 77.06 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
76 BADet code 81.61 % 89.28 % 76.58 % 0.14 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.
77 FromVoxelToPoint code 81.58 % 88.53 % 77.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
78 H^23D R-CNN code 81.55 % 90.43 % 77.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
79 FARP-Net code 81.53 % 88.36 % 78.98 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
T. Xie, L. Wang, K. Wang, R. Li, X. Zhang, H. Zhang, L. Yang, H. Liu and J. Li: FARP-Net: Local-Global Feature Aggregation and Relation-Aware Proposals for 3D Object Detection. IEEE Transactions on Multimedia 2023.
80 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 81.46 % 88.25 % 76.96 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
81 P2V-RCNN 81.45 % 88.34 % 77.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
82 New_VLGCL code 81.45 % 90.48 % 77.14 % 0.4 s 1 core @ 2.5 Ghz (Python)
83 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 81.43 % 90.25 % 76.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
84 ... code 81.39 % 90.57 % 76.96 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
85 2025AAAI-SSLfusion code 81.36 % 90.23 % 76.56 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
86 XView 81.35 % 89.21 % 76.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
87 RangeRCNN
This method makes use of Velodyne laser scans.
 81.33 % 88.47 % 77.09 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
88 CAT-Det 81.32 % 89.87 % 76.68 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
89 PASS-PV-RCNN-Plus 81.28 % 87.65 % 76.79 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
90 SparseFormer code 81.27 % 88.89 % 76.25 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
91 VLGCL_NoText code 81.21 % 90.35 % 77.00 % 0.3 s 1 core @ 2.5 Ghz (Python)
92 VPFNet code 80.97 % 88.51 % 76.74 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
C. Wang, H. Chen, Y. Chen, P. Hsiao and L. Fu: VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2024.
93 Sem-Aug
This method makes use of Velodyne laser scans.
 80.77 % 89.41 % 75.90 % 0.1 s GPU @ 2.5 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.
94 StructuralIF 80.69 % 87.15 % 76.26 % 0.02 s 8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
95 CLOCs_PVCas code 80.67 % 88.94 % 77.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
96 PointVit V2 80.54 % 89.81 % 74.96 % .006 s 1 core @ 2.5 Ghz (Python + C/C++)
V. Sood, Bnalin and G. Pandey: Point Virtual Transformer. 2026.
97 SVGA-Net 80.47 % 87.33 % 75.91 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
98 SRDL 80.38 % 87.73 % 76.27 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
99 Fast-CLOCs 80.35 % 89.10 % 76.99 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
100 SPANet 80.34 % 91.05 % 74.89 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
101 IA-SSD (single) code 80.32 % 88.87 % 75.10 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
102 CIA-SSD
This method makes use of Velodyne laser scans.
 code 80.28 % 89.59 % 72.87 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
103 IA-SSD (multi) code 80.13 % 88.34 % 75.04 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
104 EBM3DOD code 80.12 % 91.05 % 72.78 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
105 3D-CVF at SPA
This method makes use of Velodyne laser scans.
 code 80.05 % 89.20 % 73.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
106 PointVit P1 79.97 % 89.22 % 72.64 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
V. Sood, Bnalin and G. Pandey: Point Virtual Transformer. 2026.
107 PointVit V1 79.93 % 91.16 % 72.51 % .006 s 1 core @ 2.5 Ghz (Python + C/C++)
V. Sood, Bnalin and G. Pandey: Point Virtual Transformer. 2026.
108 SIF 79.88 % 86.84 % 75.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
109 RangeIoUDet
This method makes use of Velodyne laser scans.
 79.80 % 88.60 % 76.76 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
110 SA-SSD code 79.79 % 88.75 % 74.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
111 STD code 79.71 % 87.95 % 75.09 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
112 MGAF-3DSSD code 79.68 % 88.16 % 72.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
113 Struc info fusion II 79.59 % 88.97 % 72.51 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
114 3DSSD code 79.57 % 88.36 % 74.55 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
115 EBM3DOD baseline code 79.52 % 88.80 % 72.30 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
116 Struc info fusion I 79.49 % 88.70 % 74.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
117 MFusion 79.49 % 88.14 % 76.44 % 0.11 s 1 core @ 2.5 Ghz (Python)
118 Point-GNN
This method makes use of Velodyne laser scans.
 code 79.47 % 88.33 % 72.29 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
119 DFAF3D 79.37 % 88.59 % 72.21 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
120 SSL-PointGNN code 79.36 % 87.78 % 74.15 % 0.56 s GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
121 EPNet code 79.28 % 89.81 % 74.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
122 DVFENet 79.18 % 86.20 % 74.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
123 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 79.05 % 87.45 % 76.14 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
124 GD-MAE 79.03 % 88.14 % 73.55 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, T. He, J. Liu, H. Chen, B. Wu, B. Lin, X. He and W. Ouyang: GD-MAE: Generative Decoder for MAE Pre- training on LiDAR Point Clouds. CVPR 2023.
125 3D IoU-Net 79.03 % 87.96 % 72.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
126 Voxel RCNN* code 79.01 % 87.56 % 76.16 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
127 SERCNN
This method makes use of Velodyne laser scans.
 78.96 % 87.74 % 74.30 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
128 ACDet code 78.85 % 88.47 % 73.86 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
129 MVAF-Net code 78.71 % 87.87 % 75.48 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
130 LinkNet3D 78.54 % 87.22 % 74.36 % 0.04 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy, B. Sivaselvan and S. Bakshi: LinkNet3D: An efficient end-to-end network for 3D object detection. Computers and Electrical Engineering 2026.
131 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 78.49 % 87.81 % 73.51 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
132 CLOCs_SecCas 78.45 % 86.38 % 72.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
133 Patches - EMP
This method makes use of Velodyne laser scans.
 78.41 % 89.84 % 73.15 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
134 HotSpotNet 78.31 % 87.60 % 73.34 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
135 Sem-Aug-PointRCNN++ 78.06 % 86.69 % 73.85 % 0.1 s 8 cores @ 3.0 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.
136 CenterNet3D 77.90 % 86.20 % 73.03 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
137 NoText_VLGCL code 77.90 % 88.99 % 73.48 % 0.2 s 1 core @ 2.5 Ghz (Python)
138 fshe_voxeln_1 77.75 % 85.93 % 73.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
139 work6_new1 77.65 % 86.02 % 72.39 % 0.5 s GPU @ 2.5 Ghz (Python)
140 UberATG-MMF
This method makes use of Velodyne laser scans.
 77.43 % 88.40 % 70.22 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
141 Associate-3Ddet code 77.40 % 85.99 % 70.53 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
142 Fast Point R-CNN
This method makes use of Velodyne laser scans.
 77.40 % 85.29 % 70.24 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
143 RangeDet (Official) code 77.36 % 85.41 % 72.60 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
144 CS3D 77.35 % 85.51 % 72.24 % 0.5 s 1 core @ 2.5 Ghz (Python)
145 MSFASA-3DNet 77.21 % 86.06 % 71.89 % 0.03 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: MSFASA-3DNet: Multi-Scale Feature Aggregation and Spatial Attention for 3D Object Detection. IEEE Transactions on Artificial Intelligence 2026.
146 Patches
This method makes use of Velodyne laser scans.
 77.20 % 88.67 % 71.82 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
147 DynaMo3D 76.98 % 85.69 % 72.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
148 fsh_l_1_4 76.91 % 83.37 % 73.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
149 AARMOD 76.90 % 87.70 % 69.62 % 0.1 s 1 core @ 2.5 Ghz (Python)
150 SeSame-point code 76.83 % 85.25 % 71.60 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
151 HRI-VoxelFPN 76.70 % 85.64 % 69.44 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
152 SARPNET 76.64 % 85.63 % 71.31 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
153 saf_u 76.62 % 83.27 % 72.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
154 3D IoU Loss
This method makes use of Velodyne laser scans.
 76.50 % 86.16 % 71.39 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
155 F-ConvNet
This method makes use of Velodyne laser scans.
 code 76.39 % 87.36 % 66.69 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
156 fsh_saf_1 76.25 % 83.77 % 73.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 SegVoxelNet 76.13 % 86.04 % 70.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
158 S-AT GCN 76.04 % 83.20 % 71.17 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
159 TANet code 75.94 % 84.39 % 68.82 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
160 PointRGCN 75.73 % 85.97 % 70.60 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
161 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 75.64 % 86.96 % 70.70 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
162 Fade 3D code 75.57 % 85.85 % 70.47 % 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.
163 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 75.43 % 86.10 % 68.88 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
164 BASE
This method uses stereo information.
This method makes use of Velodyne laser scans.
 75.41 % 83.76 % 69.12 % N/A s GPU @ 2.5 Ghz (Python)
165 R-GCN 75.26 % 83.42 % 68.73 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
166 PL++: PV-RCNN++
This method uses stereo information.
This method makes use of Velodyne laser scans.
 75.23 % 86.60 % 70.34 % 0.342 s RTX 4060Ti (Python)
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.
167 epBRM
This method makes use of Velodyne laser scans.
 code 75.15 % 85.00 % 69.84 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
168 SeSame-voxel code 75.05 % 81.51 % 70.53 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
169 MAFF-Net(DAF-Pillar) 75.04 % 85.52 % 67.61 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
170 PASS-PointPillar 74.85 % 84.72 % 69.05 % 1 s 1 core @ 2.5 Ghz (C/C++)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
171 PI-RCNN 74.82 % 84.37 % 70.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
172 XPillars
This method makes use of Velodyne laser scans.
 74.78 % 83.53 % 69.79 % 0.02 s GPU @ 2.5 Ghz (Python)
173 mmFUSION code 74.38 % 85.24 % 69.43 % 1s 1 core @ 2.5 Ghz (Python)
J. Ahmad and A. Del Bue: mmFUSION: Multimodal Fusion for 3D Objects Detection. arXiv preprint arXiv:2311.04058 2023.
174 PointPillars
This method makes use of Velodyne laser scans.
 code 74.31 % 82.58 % 68.99 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
175 PCNet3D++ 74.19 % 84.00 % 69.65 % 0.05 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: PCNet3D++: A pillar-based cascaded 3D object detection model with an enhanced 2D backbone. Image and Vision Computing 2025.
176 HINTED code 74.13 % 84.00 % 67.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, W. Ye, H. Wu, S. Zhao, L. Xing, X. Huang, J. Deng, X. Li, C. Wen and C. Wang: HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely- Supervised 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
177 ARPNET 74.04 % 84.69 % 68.64 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
178 GBC2DNet 74.01 % 82.35 % 69.91 % 0.05 s GPU @ 2.5 Ghz (Python)
179 Harmonic PointPillar code 73.96 % 82.26 % 69.21 % 0.01 s 1 core @ 2.5 Ghz (Python)
H. Zhang, J. Mekala, Z. Nain, J. Park and H. Jung: 3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection for V2X Orchestration. will submit to IEEE Transactions on Vehicular Technology 2022.
180 M3DNet 73.87 % 83.30 % 68.70 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
T. Prasanth, R. Padhy and B. Sivaselvan: M3DNet: Minimalist 3D Detection Backbone for Efficient and Accurate 3D Object Detection. Proceedings of the 18th International Conference on Agents and Artificial Intelligence (ICAART) 2026.
181 SeSame-pillar code 73.85 % 83.88 % 68.65 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
182 VPFnet 73.83 % 82.80 % 69.29 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
183 PC-CNN-V2
This method makes use of Velodyne laser scans.
 73.79 % 85.57 % 65.65 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
184 C-GCN 73.62 % 83.49 % 67.01 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
185 PCNet3D 73.58 % 83.22 % 68.19 % 0.05 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: PCNet3D: A Pillar Based Cascaded 3D Object Detection Model Using LiDAR Point Cloud. International Conference on Computer Vision and Image Processing 2024.
186 3DBN
This method makes use of Velodyne laser scans.
 73.53 % 83.77 % 66.23 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
187 PointRGBNet 73.49 % 83.99 % 68.56 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
188 SCNet
This method makes use of Velodyne laser scans.
 73.17 % 83.34 % 67.93 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
189 SeSame-pillar w/scor code 73.15 % 82.32 % 66.64 % N/A s 1 core @ 2.5 Ghz (C/C++)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
190 PointPillars_mmdet3d 73.13 % 83.51 % 68.02 % 0.03 s 1 core @ 2.5 Ghz (Python)
191 PFF3D
This method makes use of Velodyne laser scans.
 code 72.93 % 81.11 % 67.24 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
192 DASS 72.31 % 81.85 % 65.99 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
193 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 71.76 % 83.07 % 65.73 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
194 PointPainting
This method makes use of Velodyne laser scans.
 71.70 % 82.11 % 67.08 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
195 AFCAP 70.69 % 78.33 % 66.70 % 0.1 s 1 core @ 2.5 Ghz (Python)
196 WS3D
This method makes use of Velodyne laser scans.
 70.59 % 80.99 % 64.23 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
197 DSFNet 70.44 % 80.65 % 65.03 % 0.03 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: LiDAR Sensor-Based Dual-Scale Fusion Approach for Bird’s-Eye View Sensing in Autonomous Vehicles. IEEE Sensors Letters 2025.
198 F-PointNet
This method makes use of Velodyne laser scans.
 code 69.79 % 82.19 % 60.59 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
199 EOTL code 69.13 % 79.97 % 58.57 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
200 UberATG-ContFuse
This method makes use of Velodyne laser scans.
 68.78 % 83.68 % 61.67 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
201 M-VPFnet 68.15 % 79.24 % 63.42 % 0.05 s 1 core @ 2.5 Ghz (Python)
202 MLOD
This method makes use of Velodyne laser scans.
 code 67.76 % 77.24 % 62.05 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
203 DSGN++
This method uses stereo information.
 code 67.37 % 83.21 % 59.91 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
204 DMF
This method uses stereo information.
 67.33 % 77.55 % 62.44 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
205 AVOD
This method makes use of Velodyne laser scans.
 code 66.47 % 76.39 % 60.23 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
206 StereoDistill 66.39 % 81.66 % 57.39 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
207 MMLAB LIGA-Stereo
This method uses stereo information.
 code 64.66 % 81.39 % 57.22 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
208 BirdNet+
This method makes use of Velodyne laser scans.
 code 64.04 % 76.15 % 59.79 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
209 MV3D
This method makes use of Velodyne laser scans.
 63.63 % 74.97 % 54.00 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
210 SNVC
This method uses stereo information.
 code 61.34 % 78.54 % 54.23 % 1 s GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
211 RCD 60.56 % 70.54 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
212 SeSame-point w/score code 56.92 % 74.30 % 48.14 % N/A s 1 core @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
213 A3DODWTDA
This method makes use of Velodyne laser scans.
 code 56.82 % 62.84 % 48.12 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
214 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
 code 54.88 % 68.38 % 49.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
215 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
 54.54 % 68.35 % 49.16 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
216 CDN
This method uses stereo information.
 code 54.22 % 74.52 % 46.36 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
217 CG-Stereo
This method uses stereo information.
 53.58 % 74.39 % 46.50 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
218 DSGN
This method uses stereo information.
 code 52.18 % 73.50 % 45.14 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
219 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 51.85 % 70.14 % 50.03 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
220 SG3D 47.91 % 74.23 % 42.93 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
221 Complexer-YOLO
This method makes use of Velodyne laser scans.
 47.34 % 55.93 % 42.60 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
222 SeSame-voxel w/score code 47.14 % 61.57 % 41.06 % N/A s GPU @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
223 CPD(unsupervised) code 47.04 % 68.57 % 44.13 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
H. Wu, S. Zhao, X. Huang, C. Wen, X. Li and C. Wang: Commonsense Prototype for Outdoor Unsupervised 3D Object Detection. CVPR 2024.
224 ESGN
This method uses stereo information.
 46.39 % 65.80 % 38.42 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
225 Disp R-CNN (velo)
This method uses stereo information.
 code 45.78 % 68.21 % 37.73 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
226 CDN-PL++
This method uses stereo information.
 44.86 % 64.31 % 38.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
227 DDStereo
This method uses stereo information.
 43.96 % 62.40 % 36.14 % 0.02 s GPU @ 2.5 Ghz (Python)
228 Disp R-CNN
This method uses stereo information.
 code 43.27 % 67.02 % 36.43 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
229 Pseudo-LiDAR++
This method uses stereo information.
 code 42.43 % 61.11 % 36.99 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
230 EfficientStereo-3D 41.77 % 64.96 % 35.00 % 0.06 s 1 core @ 2.5 Ghz (Python)
231 YOLOStereo3D
This method uses stereo information.
 code 41.25 % 65.68 % 30.42 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
232 StereoDETR
This method uses stereo information.
 code 41.17 % 59.45 % 35.13 % 0.02 s GPU @ 2.5 Ghz (Python)
S. Mu, Z. Gu, Z. Ai, A. Liu, Y. Gao and S. Xu: StereoDETR: Stereo-based Transformer for 3D Object Detection. 2025.
233 RT3D-GMP
This method uses stereo information.
 38.76 % 45.79 % 30.00 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
234 ZoomNet
This method uses stereo information.
 code 38.64 % 55.98 % 30.97 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
235 OC Stereo
This method uses stereo information.
 code 37.60 % 55.15 % 30.25 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
236 Pseudo-Lidar
This method uses stereo information.
 code 34.05 % 54.53 % 28.25 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
237 Stereo CenterNet
This method uses stereo information.
 31.30 % 49.94 % 25.62 % 0.04 s GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
238 Stereo R-CNN
This method uses stereo information.
 code 30.23 % 47.58 % 23.72 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
239 BirdNet
This method makes use of Velodyne laser scans.
 27.26 % 40.99 % 25.32 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
240 DA3D+KM3D+v2-99 code 26.80 % 34.72 % 23.05 % 0.120s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
241 CIE + DM3D 25.02 % 35.96 % 21.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Ananimities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
242 MonoPro 23.73 % 33.42 % 20.76 % 0.01 s 1 core @ 2.5 Ghz (Python)
243 RT3DStereo
This method uses stereo information.
 23.28 % 29.90 % 18.96 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
244 DA3D+KM3D code 22.08 % 30.83 % 19.20 % 0.02 s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
245 LeAD-M3D 21.21 % 30.76 % 18.75 % 0.0079 s 1 core @ 2.5 Ghz (Python)
246 MonoDTF 21.19 % 32.02 % 18.80 % 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.
247 CIE 20.95 % 31.55 % 17.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
248 DA3D code 20.47 % 27.76 % 17.89 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
249 STLM3D 20.32 % 29.08 % 17.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
250 MonoIA code 20.29 % 29.52 % 17.93 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
251 MonoCD-E code 20.26 % 28.49 % 17.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
252 MonoCtrl_MonoLSS 20.05 % 27.64 % 17.59 % 0.04 s 1 core @ 2.5 Ghz (Python)
253 MonoDLGD 19.87 % 29.11 % 17.74 % 0.04 s GPU @ 2.5 Ghz (Python)
254 MonoMIP 19.87 % 28.21 % 16.91 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
255 M5_3D 19.84 % 30.43 % 17.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
256 MonoCLUE code 19.70 % 27.94 % 16.69 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
257 MonoHID 19.67 % 28.05 % 16.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
258 MonoHPE-Mask 19.59 % 27.07 % 17.36 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
259 RARE 19.57 % 28.83 % 17.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
260 MonoLSPF 19.51 % 27.34 % 17.26 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
261 MonoHPE 19.51 % 27.86 % 16.58 % 0.04 s 1 core @ 2.5 Ghz (Python)
262 MonoDGC code 19.42 % 27.13 % 16.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
263 MonoGeo code 19.34 % 27.07 % 16.57 % 0.14 s GPU @ 2.5 Ghz (Python)
264 AMNet+DDAD15M code 19.26 % 26.26 % 17.05 % 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.
265 MonoLSS 19.15 % 26.11 % 16.94 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
266 RT3D
This method makes use of Velodyne laser scans.
 19.14 % 23.74 % 18.86 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
267 MonoCoP code 19.11 % 27.54 % 16.33 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhang, A. Kumar, G. Ganesan and X. Liu: Unleashing the Power of Chain-of- Prediction for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026.
268 MonoGeo code 18.96 % 27.37 % 16.28 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
269 MF 18.94 % 27.19 % 16.17 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
270 NeurOCS 18.94 % 29.89 % 15.90 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Min, B. Zhuang, S. Schulter, B. Liu, E. Dunn and M. Chandraker: NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization. CVPR 2023.
271 IDEAL-M3D 18.87 % 27.06 % 16.73 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Meier, F. Günther, R. Marin, O. Dhaouadi, J. Kaiser and D. Cremers: IDEAL-M3D: Instance Diversity- Enriched Active Learning for Monocular 3D Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026.
272 MonoLiG code 18.86 % 24.90 % 16.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
273 MonoCLUE 18.77 % 26.30 % 16.10 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
274 MonoCLUE_all 18.70 % 26.59 % 15.99 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
275 MonoCtrl_MonoLSS(re) 18.69 % 27.03 % 15.81 % 0.04 s 1 core @ 2.5 Ghz (Python)
276 CMKD code 18.69 % 28.55 % 16.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
277 DetAny3D code 18.67 % 26.89 % 15.48 % 0.58 s 1 core @ 2.5 Ghz (Python)
278 Monodgc-3classes code 18.62 % 26.43 % 15.89 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
279 Mix-Teaching code 18.54 % 26.89 % 15.79 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
280 MonoCtrl_MonoDistill 18.41 % 26.67 % 15.97 % 0.06 s 1 core @ 2.5 Ghz (Python)
281 StereoFENet
This method uses stereo information.
 18.41 % 29.14 % 14.20 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
282 AMNet code 18.36 % 26.09 % 15.86 % 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.
283 MonoCtrl_MonoCD 18.32 % 25.86 % 15.42 % 0.06 s 1 core @ 2.5 Ghz (Python)
284 MonoGMM 18.25 % 25.37 % 15.63 % 35ms 1 core @ 2.5 Ghz (C/C++)
285 MonoCtrl_MonoDistill 18.24 % 22.16 % 16.63 % 0.06 s 1 core @ 2.5 Ghz (Python)
286 PS-SVDM 18.13 % 29.22 % 15.35 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
287 MonoSample (DID-M3D) code 18.05 % 28.63 % 15.19 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
J. Qiao, B. Liu, J. Yang, B. Wang, S. Xiu, X. Du and X. Nie: MonoSample: Synthetic 3D Data Augmentation Method in Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2024.
288 MonoSC 18.03 % 24.57 % 15.80 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
289 MonoCtrl_MonoDistill 17.92 % 25.90 % 16.66 % 0.06 s 1 core @ 2.5 Ghz (Python)
290 MonoLSS(retrained) 17.89 % 25.09 % 15.18 % 0.02 s 1 core @ 2.5 Ghz (Python)
291 LPCG-Monoflex code 17.80 % 25.56 % 15.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
292 PS-fld code 17.74 % 23.74 % 15.14 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
293 fdaa11 17.65 % 26.09 % 15.54 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
294 MonoSKD code 17.35 % 28.43 % 15.01 % 0.04 s 1 core @ 2.5 Ghz (Python)
S. Wang and J. Zheng: MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficient. ECAI 2023.
295 MonoDDE 17.14 % 24.93 % 15.10 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
296 MonoNeRD code 17.13 % 22.75 % 15.63 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
297 OPA-3D code 17.05 % 24.60 % 14.25 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
298 Mobile Stereo R-CNN
This method uses stereo information.
 17.04 % 26.97 % 13.26 % 1.8 s NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
299 DD3D code 16.87 % 23.19 % 14.36 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
300 ADD code 16.81 % 25.61 % 13.79 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Wu, Y. Wu, J. Pu, X. Li and X. Wang: Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection. AAAI2023 .
301 MonOri code 16.77 % 25.20 % 14.45 % 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.
302 MonoUNI code 16.73 % 24.75 % 13.49 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
303 MonoCD code 16.59 % 25.53 % 14.53 % n/a s 1 core @ 2.5 Ghz (Python)
L. Yan, P. Yan, S. Xiong, X. Xiang and Y. Tan: MonoCD: Monocular 3D Object Detection with Complementary Depths. CVPR 2024.
304 DID-M3D code 16.29 % 24.40 % 13.75 % 0.04 s 1 core @ 2.5 Ghz (Python)
L. Peng, X. Wu, Z. Yang, H. Liu and D. Cai: DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection. ECCV 2022.
305 MonoDETR code 16.26 % 24.52 % 13.93 % 0.04 s 1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.
306 MonoFRD 16.24 % 21.11 % 14.97 % 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.
307 DCD code 15.90 % 23.81 % 13.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Y. Li, Y. Chen, J. He and Z. Zhang: Densely Constrained Depth Estimator for Monocular 3D Object Detection. European Conference on Computer Vision 2022.
308 MonoDTR 15.39 % 21.99 % 12.73 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
309 GUPNet code 15.02 % 22.26 % 13.12 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
310 Cube R-CNN code 15.01 % 23.59 % 12.56 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
311 HomoLoss(monoflex) code 14.94 % 21.75 % 13.07 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
312 SGM3D code 14.65 % 22.46 % 12.97 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
313 MonoDSSMs-A 14.55 % 21.47 % 11.78 % 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.
314 MDSNet 14.46 % 24.30 % 11.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
315 DEVIANT code 14.46 % 21.88 % 11.89 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
316 DLE code 14.33 % 24.23 % 10.30 % 0.06 s NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
317 AutoShape code 14.17 % 22.47 % 11.36 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
318 MonoDSSMs-M 14.15 % 19.80 % 11.56 % 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.
319 MonoFlex 13.89 % 19.94 % 12.07 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
320 MonoEF 13.87 % 21.29 % 11.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
321 MonoRCNN++ code 13.72 % 20.08 % 11.34 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
322 DFR-Net 13.63 % 19.40 % 10.35 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
323 temp 13.53 % 18.63 % 11.13 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
324 PS-SVDM 13.49 % 20.83 % 11.18 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
325 CaDDN code 13.41 % 19.17 % 11.46 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
326 PCT code 13.37 % 21.00 % 11.31 % 0.045 s 1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
327 Ground-Aware code 13.25 % 21.65 % 9.91 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
328 FMF-occlusion-net 13.12 % 20.28 % 9.56 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
329 Aug3D-RPN 12.99 % 17.82 % 9.78 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
330 HomoLoss(imvoxelnet) code 12.99 % 20.10 % 10.50 % 0.20 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
331 DDMP-3D 12.78 % 19.71 % 9.80 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
332 Kinematic3D code 12.72 % 19.07 % 9.17 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
333 MonoRCNN code 12.65 % 18.36 % 10.03 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
334 GrooMeD-NMS code 12.32 % 18.10 % 9.65 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
335 MonoRUn code 12.30 % 19.65 % 10.58 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
336 monodle code 12.26 % 17.23 % 10.29 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
337 YoloMono3D code 12.06 % 18.28 % 8.42 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
338 IAFA 12.01 % 17.81 % 10.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
339 MonOAPC 12.00 % 18.77 % 9.75 % 0035 s 1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
340 GAC3D 12.00 % 17.75 % 9.15 % 0.25 s 1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
341 CMAN 11.87 % 17.77 % 9.16 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
342 PGD-FCOS3D code 11.76 % 19.05 % 9.39 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
343 D4LCN code 11.72 % 16.65 % 9.51 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
344 KM3D code 11.45 % 16.73 % 9.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
345 BEVHeight++ code 11.26 % 16.69 % 9.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, T. Tang, J. Li, P. Chen, K. Yuan, L. Wang, Y. Huang, X. Zhang and K. Yu: Bevheight++: Toward robust visual centric 3d object detection. arXiv preprint arXiv:2309.16179 2023.
346 RefinedMPL 11.14 % 18.09 % 8.94 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
347 PatchNet code 11.12 % 15.68 % 10.17 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
348 ImVoxelNet code 10.97 % 17.15 % 9.15 % 0.2 s GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
349 AM3D 10.74 % 16.50 % 9.52 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
350 MonoCD+DGWA 10.35 % 15.85 % 7.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
351 RTM3D code 10.34 % 14.41 % 8.77 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
352 FA-MonoCD 10.08 % 14.65 % 7.90 % 0.15 s GPU @ 2.5 Ghz (Python)
353 MonoPair 9.99 % 13.04 % 8.65 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
354 Neighbor-Vote 9.90 % 15.57 % 8.89 % 0.1 s GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
355 SMOKE code 9.76 % 14.03 % 7.84 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
356 M3D-RPN code 9.71 % 14.76 % 7.42 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
357 QD-3DT
This is an online method (no batch processing).
 code 9.33 % 12.81 % 7.86 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
358 TopNet-HighRes
This method makes use of Velodyne laser scans.
 9.28 % 12.67 % 7.95 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
359 MonoCD code 9.03 % 13.10 % 6.92 % 0.04 s GPU @ 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.
360 MonoCInIS 7.94 % 15.82 % 6.68 % 0,13 s GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
361 Plane-Constraints code 7.88 % 11.29 % 6.48 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
362 SS3D 7.68 % 10.78 % 6.51 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
363 MonoCInIS 7.66 % 15.21 % 6.24 % 0,14 s GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
364 Mono3D_PLiDAR code 7.50 % 10.76 % 6.10 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
365 MonoPSR code 7.25 % 10.76 % 5.85 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
366 Decoupled-3D 7.02 % 11.08 % 5.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
367 VoxelJones code 6.35 % 7.39 % 5.80 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
368 MonoGRNet code 5.74 % 9.61 % 4.25 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
369 A3DODWTDA (image) code 5.27 % 6.88 % 4.45 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
370 MonoFENet 5.14 % 8.35 % 4.10 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
371 TLNet (Stereo)
This method uses stereo information.
 code 4.37 % 7.64 % 3.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
372 CSoR
This method makes use of Velodyne laser scans.
 4.06 % 5.61 % 3.17 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
373 Shift R-CNN (mono) code 3.87 % 6.88 % 2.83 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
374 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
375 SparVox3D 3.20 % 5.27 % 2.56 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
376 TopNet-UncEst
This method makes use of Velodyne laser scans.
 3.02 % 3.24 % 2.26 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
377 GS3D 2.90 % 4.47 % 2.47 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
378 3D-GCK 2.52 % 3.27 % 2.11 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
379 WeakM3D code 2.26 % 5.03 % 1.63 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
380 ROI-10D 2.02 % 4.32 % 1.46 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
381 FQNet 1.51 % 2.77 % 1.01 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
382 3D-SSMFCNN code 1.41 % 1.88 % 1.11 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
383 tmp 0.00 % 0.00 % 0.00 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
384 EAEPNet 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
385 mBoW
This method makes use of Velodyne laser scans.
 0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 CasA++ code 49.29 % 56.33 % 46.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
2 TED code 49.21 % 55.85 % 46.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
3 ImagePG code 48.81 % 56.66 % 45.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 UPIDet code 48.77 % 55.59 % 46.12 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
5 VPFNet code 48.36 % 54.65 % 44.98 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
C. Wang, H. Chen, Y. Chen, P. Hsiao and L. Fu: VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2024.
6 WinMamba code 48.04 % 56.10 % 45.62 % 0.1 s 1 core @ 2.5 Ghz (Python)
7 LoGoNet code 47.43 % 53.07 % 45.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
8 CasA code 47.09 % 54.04 % 44.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
9 EQ-PVRCNN code 47.02 % 55.84 % 42.94 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
10 PiFeNet code 46.71 % 56.39 % 42.71 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.
11 USVLab BSAODet code 46.50 % 52.69 % 43.10 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
12 ACFNet 46.36 % 54.62 % 42.57 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
13 DPPFA-Net 46.14 % 53.58 % 42.59 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Wang, X. Kong, H. Nishikawa, Q. Lian and H. Tomiyama: Dynamic Point-Pixel Feature Alignment for Multi-modal 3D Object Detection. IEEE Internet of Things Journal 2023.
14 SparseFormer code 45.88 % 53.01 % 43.27 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
15 ... code 45.86 % 54.39 % 43.40 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
16 CAT-Det 45.44 % 54.26 % 41.94 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
17 HotSpotNet 45.37 % 53.10 % 41.47 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
18 MLF-DET 45.29 % 50.86 % 42.05 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
19 LumiNet 45.26 % 53.54 % 41.55 % 0.1 s 1 core @ 2.5 Ghz (Python)
20 RobusTor3D 44.97 % 52.08 % 42.68 % ... s 1 core @ 2.5 Ghz (C/C++)
21 ACDet code 44.79 % 53.41 % 41.96 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
22 DynaMo3D 44.73 % 53.92 % 41.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
23 2025AAAI-SSLfusion code 44.72 % 52.39 % 42.35 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
24 EPNet++ 44.38 % 52.79 % 41.29 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
25 TANet code 44.34 % 53.72 % 40.49 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
26 3DSSD code 44.27 % 54.64 % 40.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
27 CEF code 44.01 % 52.30 % 41.42 % 0.03 s 1 core @ 2.5 Ghz (Python)
28 New_VLGCL code 43.93 % 51.26 % 41.60 % 0.4 s 1 core @ 2.5 Ghz (Python)
29 Point-GNN
This method makes use of Velodyne laser scans.
 code 43.77 % 51.92 % 40.14 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
30 VLGCL_NoText code 43.70 % 50.82 % 41.50 % 0.3 s 1 core @ 2.5 Ghz (Python)
31 auto_test 43.57 % 51.32 % 41.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
32 3ONet 43.45 % 52.81 % 39.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
33 F-ConvNet
This method makes use of Velodyne laser scans.
 code 43.38 % 52.16 % 38.80 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
34 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 43.35 % 53.10 % 40.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
35 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 43.29 % 52.17 % 40.29 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
36 FromVoxelToPoint code 43.28 % 51.80 % 40.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
37 VMVS
This method makes use of Velodyne laser scans.
 43.27 % 53.44 % 39.51 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
38 P2V-RCNN 43.19 % 50.91 % 40.81 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
39 MGAF-3DSSD code 43.09 % 50.65 % 39.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
40 Frustum-PointPillars code 42.89 % 51.22 % 39.28 % 0.06 s 4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
41 Fast-CLOCs 42.72 % 52.10 % 39.08 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
42 HMFI code 42.65 % 50.88 % 39.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
43 STD code 42.47 % 53.29 % 38.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
44 kfc 42.37 % 49.80 % 38.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 42.27 % 50.46 % 39.04 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
46 SemanticVoxels 42.19 % 50.90 % 39.52 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
47 MoAd code 42.19 % 48.89 % 38.84 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
48 F-PointNet
This method makes use of Velodyne laser scans.
 code 42.15 % 50.53 % 38.08 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
49 PASS-PV-RCNN-Plus 41.95 % 47.66 % 38.90 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
50 PointPillars
This method makes use of Velodyne laser scans.
 code 41.92 % 51.45 % 38.89 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
51 fsh_l_1_4 41.90 % 50.15 % 39.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 DPFusion code 41.85 % 49.04 % 38.29 % 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.
53 fshe_voxeln_1 41.65 % 49.01 % 38.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
54 epBRM
This method makes use of Velodyne laser scans.
 code 41.52 % 49.17 % 39.08 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
55 PG-RCNN code 41.04 % 47.99 % 38.71 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
56 IA-SSD (single) code 41.03 % 47.90 % 37.98 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
57 DFAF3D 40.99 % 47.58 % 37.65 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
58 PointPainting
This method makes use of Velodyne laser scans.
 40.97 % 50.32 % 37.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
59 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 40.89 % 46.97 % 38.80 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
60 Voxel RCNN* code 40.68 % 48.70 % 38.51 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
61 fsh_saf_1 40.66 % 48.69 % 37.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 PDV code 40.56 % 47.80 % 38.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
63 SVGA-Net 40.39 % 48.48 % 37.92 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
64 EOTL code 40.11 % 48.65 % 35.99 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
65 M3DeTR code 39.94 % 45.70 % 37.66 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
66 NoText_VLGCL code 39.75 % 46.97 % 36.98 % 0.2 s 1 core @ 2.5 Ghz (Python)
67 XPillars
This method makes use of Velodyne laser scans.
 39.57 % 47.99 % 36.47 % 0.02 s GPU @ 2.5 Ghz (Python)
68 SRDL 39.43 % 47.30 % 36.99 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
69 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 39.37 % 47.98 % 36.01 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
70 ARPNET 39.31 % 48.32 % 35.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
71 saf_u 39.07 % 46.13 % 36.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 L-AUG 39.07 % 46.76 % 35.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
73 IA-SSD (multi) code 39.03 % 46.51 % 35.61 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
74 work6_new1 39.00 % 46.12 % 36.53 % 0.5 s GPU @ 2.5 Ghz (Python)
75 SIF 38.74 % 46.23 % 36.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
76 SCNet
This method makes use of Velodyne laser scans.
 38.66 % 47.83 % 35.70 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
77 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 38.58 % 46.33 % 35.71 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
78 HINTED code 37.75 % 47.33 % 34.10 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, W. Ye, H. Wu, S. Zhao, L. Xing, X. Huang, J. Deng, X. Li, C. Wen and C. Wang: HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely- Supervised 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
79 VPFnet 37.63 % 45.71 % 34.55 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
80 DVFENet 37.50 % 43.55 % 35.33 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
81 MLOD
This method makes use of Velodyne laser scans.
 code 37.47 % 47.58 % 35.07 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
82 PCNet3D++ 37.47 % 45.22 % 34.95 % 0.05 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: PCNet3D++: A pillar-based cascaded 3D object detection model with an enhanced 2D backbone. Image and Vision Computing 2025.
83 SeSame-voxel code 37.37 % 46.53 % 33.56 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
84 S-AT GCN 37.37 % 44.63 % 34.92 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
85 LGFE 37.15 % 46.31 % 34.76 % 0.03 s 1 core @ 2.5 Ghz (Python)
86 PCNet3D 37.00 % 44.66 % 34.16 % 0.05 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: PCNet3D: A Pillar Based Cascaded 3D Object Detection Model Using LiDAR Point Cloud. International Conference on Computer Vision and Image Processing 2024.
87 M3DNet 36.93 % 45.15 % 34.27 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
T. Prasanth, R. Padhy and B. Sivaselvan: M3DNet: Minimalist 3D Detection Backbone for Efficient and Accurate 3D Object Detection. Proceedings of the 18th International Conference on Agents and Artificial Intelligence (ICAART) 2026.
88 GraphAlign(ICCV2023) code 36.89 % 41.38 % 34.95 % 0.03 s GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
89 XView 36.79 % 42.44 % 34.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
90 AFCAP 36.59 % 44.04 % 34.12 % 0.1 s 1 core @ 2.5 Ghz (Python)
91 CS3D 36.44 % 43.92 % 34.27 % 0.5 s 1 core @ 2.5 Ghz (Python)
92 MSFASA-3DNet 36.32 % 42.89 % 34.33 % 0.03 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: MSFASA-3DNet: Multi-Scale Feature Aggregation and Spatial Attention for 3D Object Detection. IEEE Transactions on Artificial Intelligence 2026.
93 BASE
This method uses stereo information.
This method makes use of Velodyne laser scans.
 36.20 % 45.62 % 33.59 % N/A s GPU @ 2.5 Ghz (Python)
94 PFF3D
This method makes use of Velodyne laser scans.
 code 36.07 % 43.93 % 32.86 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
95 Ped_Net 35.75 % 43.42 % 33.05 % 0.5 s GPU @ 2.5 Ghz (Python + C/C++)
96 M-VPFnet 35.48 % 44.22 % 32.22 % 0.05 s 1 core @ 2.5 Ghz (Python)
97 SeSame-point code 35.34 % 42.29 % 33.02 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
98 GBC2DNet 35.15 % 43.43 % 32.53 % 0.05 s GPU @ 2.5 Ghz (Python)
99 BirdNet+
This method makes use of Velodyne laser scans.
 code 35.06 % 41.55 % 32.93 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
100 DSFNet 34.60 % 43.19 % 31.85 % 0.03 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: LiDAR Sensor-Based Dual-Scale Fusion Approach for Bird’s-Eye View Sensing in Autonomous Vehicles. IEEE Sensors Letters 2025.
101 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 34.59 % 42.27 % 31.37 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
102 PL++: PV-RCNN++
This method uses stereo information.
This method makes use of Velodyne laser scans.
 33.89 % 41.53 % 31.42 % 0.342 s RTX 4060Ti (Python)
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.
103 DSGN++
This method uses stereo information.
 code 32.74 % 43.05 % 29.54 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
104 StereoDistill 32.23 % 44.12 % 28.95 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
105 PointPillars_mmdet3d 32.10 % 39.38 % 29.54 % 0.03 s 1 core @ 2.5 Ghz (Python)
106 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 31.46 % 37.99 % 29.46 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
107 SeSame-pillar code 31.00 % 37.61 % 28.86 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
108 SparsePool code 30.38 % 37.84 % 26.94 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
109 MMLAB LIGA-Stereo
This method uses stereo information.
 code 30.00 % 40.46 % 27.07 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
110 DMF
This method uses stereo information.
 29.77 % 37.21 % 27.62 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
111 SeSame-voxel w/score code 28.26 % 34.14 % 26.15 % N/A s GPU @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
112 SparsePool code 27.92 % 35.52 % 25.87 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
113 AVOD
This method makes use of Velodyne laser scans.
 code 27.86 % 36.10 % 25.76 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
114 SeSame-pillar w/scor code 27.23 % 33.87 % 25.27 % N/A s 1 core @ 2.5 Ghz (C/C++)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
115 CSW3D
This method makes use of Velodyne laser scans.
 26.64 % 33.75 % 23.34 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
116 PointRGBNet 26.40 % 34.77 % 24.03 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
117 Disp R-CNN (velo)
This method uses stereo information.
 code 25.80 % 37.12 % 22.04 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
118 Disp R-CNN
This method uses stereo information.
 code 25.40 % 35.75 % 21.79 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
119 CG-Stereo
This method uses stereo information.
 24.31 % 33.22 % 20.95 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
120 Fade 3D code 24.06 % 30.25 % 21.84 % 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.
121 SeSame-point w/score code 23.33 % 31.13 % 20.07 % N/A s 1 core @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
122 StereoDETR
This method uses stereo information.
 code 23.25 % 33.18 % 19.86 % 0.02 s GPU @ 2.5 Ghz (Python)
S. Mu, Z. Gu, Z. Ai, A. Liu, Y. Gao and S. Xu: StereoDETR: Stereo-based Transformer for 3D Object Detection. 2025.
123 DDStereo
This method uses stereo information.
 21.92 % 32.14 % 19.43 % 0.02 s GPU @ 2.5 Ghz (Python)
124 YOLOStereo3D
This method uses stereo information.
 code 19.75 % 28.49 % 16.48 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
125 SG3D 19.20 % 28.40 % 16.98 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
126 OC Stereo
This method uses stereo information.
 code 17.58 % 24.48 % 15.60 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
127 BirdNet
This method makes use of Velodyne laser scans.
 17.08 % 22.04 % 15.82 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
128 DSGN
This method uses stereo information.
 code 15.55 % 20.53 % 14.15 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
129 EfficientStereo-3D 14.02 % 20.69 % 12.24 % 0.06 s 1 core @ 2.5 Ghz (Python)
130 Complexer-YOLO
This method makes use of Velodyne laser scans.
 13.96 % 17.60 % 12.70 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
131 MonOri code 12.76 % 18.97 % 11.00 % 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.
132 RT3D-GMP
This method uses stereo information.
 11.41 % 16.23 % 10.12 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
133 MonoLSS 11.27 % 17.09 % 10.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
134 DD3D code 11.04 % 16.64 % 9.38 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
135 LeAD-M3D 10.98 % 16.68 % 9.21 % 0.0079 s 1 core @ 2.5 Ghz (Python)
136 PS-fld code 10.82 % 16.95 % 9.26 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
137 MonoHPE-Mask 10.74 % 15.83 % 9.38 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
138 MonoHPE 10.62 % 16.26 % 9.02 % 0.04 s 1 core @ 2.5 Ghz (Python)
139 fdaa11 10.58 % 16.26 % 8.96 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
140 MonoGeo code 10.54 % 16.62 % 9.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
141 CIE 10.53 % 16.19 % 8.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
142 Monodgc-3classes code 10.50 % 16.14 % 8.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
143 OPA-3D code 10.49 % 15.65 % 8.80 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
144 MonoCtrl_MonoLSS 10.49 % 16.66 % 8.94 % 0.04 s 1 core @ 2.5 Ghz (Python)
145 MonoCLUE_all 10.45 % 16.18 % 8.75 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
146 MonoUNI code 10.34 % 15.78 % 8.74 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
147 MonoCoP code 10.33 % 15.61 % 8.53 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhang, A. Kumar, G. Ganesan and X. Liu: Unleashing the Power of Chain-of- Prediction for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026.
148 MonoCLUE 10.32 % 16.03 % 8.76 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
149 MonoLSPF 10.32 % 15.90 % 8.67 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
150 ESGN
This method uses stereo information.
 10.27 % 14.05 % 9.02 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
151 MonoDTR 10.18 % 15.33 % 8.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
152 RARE 10.15 % 14.80 % 8.53 % 0.03 s 1 core @ 2.5 Ghz (Python)
153 GUPNet code 9.76 % 14.95 % 8.41 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
154 MonoFRD 8.88 % 13.86 % 7.53 % 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.
155 SGM3D code 8.81 % 13.99 % 7.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
156 CMKD code 8.79 % 13.94 % 7.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
157 AMNet+DDAD15M code 8.67 % 13.18 % 7.43 % 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.
158 CPD(unsupervised) code 8.66 % 10.87 % 7.83 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
H. Wu, S. Zhao, X. Huang, C. Wen, X. Li and C. Wang: Commonsense Prototype for Outdoor Unsupervised 3D Object Detection. CVPR 2024.
159 DEVIANT code 8.65 % 13.43 % 7.69 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
160 IDEAL-M3D 8.50 % 13.73 % 7.52 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Meier, F. Günther, R. Marin, O. Dhaouadi, J. Kaiser and D. Cremers: IDEAL-M3D: Instance Diversity- Enriched Active Learning for Monocular 3D Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026.
161 AMNet code 8.39 % 12.79 % 7.07 % 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.
162 PS-SVDM 8.33 % 12.93 % 7.20 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
163 MonoNeRD code 8.26 % 13.20 % 7.02 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
164 CaDDN code 8.14 % 12.87 % 6.76 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
165 MonoRCNN++ code 7.90 % 12.26 % 6.62 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
166 MonoPro 7.67 % 10.94 % 7.06 % 0.01 s 1 core @ 2.5 Ghz (Python)
167 HomoLoss(monoflex) code 7.66 % 11.87 % 6.82 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
168 temp 7.61 % 11.83 % 6.29 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
169 Mix-Teaching code 7.47 % 11.67 % 6.61 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
170 LPCG-Monoflex code 7.33 % 10.82 % 6.18 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
171 MonoDDE 7.32 % 11.13 % 6.67 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
172 RefinedMPL 7.18 % 11.14 % 5.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
173 MDSNet 7.09 % 10.68 % 6.06 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
174 Cube R-CNN code 6.95 % 11.17 % 5.87 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
175 PS-SVDM 6.93 % 11.16 % 5.96 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
176 TopNet-HighRes
This method makes use of Velodyne laser scans.
 6.92 % 10.40 % 6.63 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
177 MonoRUn code 6.78 % 10.88 % 5.83 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
178 MonoPair 6.68 % 10.02 % 5.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
179 monodle code 6.55 % 9.64 % 5.44 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
180 DA3D+KM3D+v2-99 code 6.32 % 9.38 % 5.54 % 0.120s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
181 MonoFlex 6.31 % 9.43 % 5.26 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
182 MonOAPC 5.87 % 8.75 % 4.84 % 0035 s 1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
183 FMF-occlusion-net 5.23 % 7.62 % 4.28 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
184 Aug3D-RPN 4.71 % 6.01 % 3.87 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
185 Shift R-CNN (mono) code 4.66 % 7.95 % 4.16 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
186 MonoPSR code 4.00 % 6.12 % 3.30 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
187 DA3D+KM3D code 3.64 % 5.60 % 3.10 % 0.02 s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
188 DFR-Net 3.62 % 6.09 % 3.39 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
189 DDMP-3D 3.55 % 4.93 % 3.01 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
190 M3D-RPN code 3.48 % 4.92 % 2.94 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
191 D4LCN code 3.42 % 4.55 % 2.83 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
192 CMAN 3.41 % 4.62 % 2.87 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
193 QD-3DT
This is an online method (no batch processing).
 code 3.37 % 5.53 % 3.02 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
194 DA3D code 2.95 % 4.62 % 2.58 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
195 MonoEF 2.79 % 4.27 % 2.21 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
196 RT3DStereo
This method uses stereo information.
 2.45 % 3.28 % 2.35 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
197 MonoLiG code 1.94 % 2.89 % 1.91 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
198 TopNet-UncEst
This method makes use of Velodyne laser scans.
 1.87 % 3.42 % 1.73 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
199 SS3D 1.78 % 2.31 % 1.48 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
200 PGD-FCOS3D code 1.49 % 2.28 % 1.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
201 SparVox3D 1.35 % 1.93 % 1.04 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
202 Plane-Constraints code 1.09 % 1.73 % 1.04 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
203 tmp 0.00 % 0.00 % 0.00 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
204 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 code 74.68 % 86.53 % 66.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 UPIDet code 74.32 % 86.74 % 67.45 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
3 TED code 74.12 % 88.82 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
4 WinMamba code 73.86 % 87.84 % 66.76 % 0.1 s 1 core @ 2.5 Ghz (Python)
5 CasA++ code 73.79 % 87.76 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
6 CasA code 73.47 % 87.91 % 66.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
7 LoGoNet code 71.70 % 84.47 % 64.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
8 auto_test 70.88 % 83.63 % 62.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
9 MLF-DET 70.71 % 83.31 % 63.71 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
10 USVLab BSAODet code 70.48 % 83.17 % 62.46 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
11 HMFI code 70.37 % 84.02 % 62.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
12 kfc 69.92 % 83.99 % 61.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
13 EQ-PVRCNN code 69.10 % 85.41 % 62.30 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
14 CAT-Det 68.81 % 83.68 % 61.45 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
15 BtcDet
This method makes use of Velodyne laser scans.
 code 68.68 % 82.81 % 61.81 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
16 RagNet3D code 68.55 % 83.84 % 61.94 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Chen, Y. Han, Z. Yan, J. Qian, J. Li and J. Yang: Ragnet3d: Learning Distinguishable Representation for Pooled Grids in 3d Object Detection. Available at SSRN 4979473 .
17 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
 code 68.54 % 82.19 % 61.33 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
18 PASS-PV-RCNN-Plus 68.45 % 80.43 % 60.93 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
19 ACFNet 68.37 % 84.29 % 62.08 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
20 3ONet 68.37 % 82.34 % 60.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
21 PG-RCNN code 67.82 % 82.77 % 61.25 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
22 PDV code 67.81 % 83.04 % 60.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
23 RangeIoUDet
This method makes use of Velodyne laser scans.
 67.77 % 83.12 % 60.26 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
24 SPG_mini
This method makes use of Velodyne laser scans.
 code 66.96 % 80.21 % 60.50 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
25 M3DeTR code 66.74 % 83.83 % 59.03 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
26 ACDet code 66.61 % 83.80 % 59.99 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
27 DPFusion code 66.47 % 79.96 % 58.47 % 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.
28 SparseFormer code 66.40 % 81.49 % 59.43 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
29 IA-SSD (single) code 66.25 % 82.36 % 59.70 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
30 HotSpotNet 65.95 % 82.59 % 59.00 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
31 DFAF3D 65.86 % 82.09 % 59.02 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
32 Fast-CLOCs 65.31 % 82.83 % 57.43 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
33 MoAd code 65.21 % 78.52 % 57.48 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
34 F-ConvNet
This method makes use of Velodyne laser scans.
 code 65.07 % 81.98 % 56.54 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
35 VLGCL_NoText code 64.96 % 81.05 % 59.68 % 0.3 s 1 core @ 2.5 Ghz (Python)
36 fsh_l_1_4 64.66 % 81.60 % 57.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 GraphAlign(ICCV2023) code 64.43 % 78.42 % 58.71 % 0.03 s GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
38 RobusTor3D 64.38 % 78.73 % 58.66 % ... s 1 core @ 2.5 Ghz (C/C++)
39 3DSSD code 64.10 % 82.48 % 56.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
40 VPFNet code 64.10 % 77.64 % 58.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
C. Wang, H. Chen, Y. Chen, P. Hsiao and L. Fu: VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2024.
41 PointPainting
This method makes use of Velodyne laser scans.
 63.78 % 77.63 % 55.89 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
42 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
 code 63.71 % 78.60 % 57.65 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
43 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 code 63.52 % 79.17 % 56.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
44 Point-GNN
This method makes use of Velodyne laser scans.
 code 63.48 % 78.60 % 57.08 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
45 MGAF-3DSSD code 63.43 % 80.64 % 55.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
46 FromVoxelToPoint code 63.41 % 81.49 % 56.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
47 P2V-RCNN 63.13 % 78.62 % 56.81 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
48 fshe_voxeln_1 63.08 % 78.65 % 56.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 HINTED code 63.01 % 76.21 % 55.85 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, W. Ye, H. Wu, S. Zhao, L. Xing, X. Huang, J. Deng, X. Li, C. Wen and C. Wang: HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely- Supervised 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
50 New_VLGCL code 62.89 % 75.37 % 58.08 % 0.4 s 1 core @ 2.5 Ghz (Python)
51 H^23D R-CNN code 62.74 % 78.67 % 55.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
52 CEF code 62.49 % 80.56 % 55.91 % 0.03 s 1 core @ 2.5 Ghz (Python)
53 Voxel RCNN* code 62.37 % 80.89 % 56.12 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
54 2025AAAI-SSLfusion code 62.32 % 78.06 % 56.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
55 LumiNet 62.31 % 80.43 % 55.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
56 SVGA-Net 62.28 % 78.58 % 54.88 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
57 SRDL 62.02 % 77.35 % 55.52 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
58 Faraway-Frustum
This method makes use of Velodyne laser scans.
 code 62.00 % 77.36 % 55.40 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
59 DVFENet 62.00 % 78.73 % 55.18 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
60 IA-SSD (multi) code 61.94 % 78.35 % 55.70 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
61 S-AT GCN 61.70 % 75.24 % 55.32 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
62 SIF 61.61 % 77.13 % 55.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
63 STD code 61.59 % 78.69 % 55.30 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
64 ... code 61.48 % 76.89 % 57.17 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
65 XPillars
This method makes use of Velodyne laser scans.
 60.68 % 74.74 % 55.12 % 0.02 s GPU @ 2.5 Ghz (Python)
66 fsh_saf_1 60.60 % 75.61 % 53.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 LinkNet3D 60.47 % 76.11 % 53.77 % 0.04 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy, B. Sivaselvan and S. Bakshi: LinkNet3D: An efficient end-to-end network for 3D object detection. Computers and Electrical Engineering 2026.
68 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 code 60.30 % 75.42 % 53.81 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
69 MSFASA-3DNet 60.30 % 75.60 % 53.83 % 0.03 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: MSFASA-3DNet: Multi-Scale Feature Aggregation and Spatial Attention for 3D Object Detection. IEEE Transactions on Artificial Intelligence 2026.
70 AFCAP 60.03 % 75.19 % 53.01 % 0.1 s 1 core @ 2.5 Ghz (Python)
71 EPNet++ 59.71 % 76.15 % 53.67 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
72 XView 59.55 % 77.24 % 53.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
73 TANet code 59.44 % 75.70 % 52.53 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
74 VPFnet 59.44 % 76.45 % 52.99 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
75 L-AUG 59.30 % 73.32 % 53.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
76 NoText_VLGCL code 58.98 % 75.70 % 53.75 % 0.2 s 1 core @ 2.5 Ghz (Python)
77 EOTL code 58.96 % 75.20 % 50.41 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
78 saf_u 58.88 % 75.80 % 51.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
 code 58.82 % 74.96 % 52.53 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
80 PCNet3D++ 58.67 % 76.16 % 52.02 % 0.05 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: PCNet3D++: A pillar-based cascaded 3D object detection model with an enhanced 2D backbone. Image and Vision Computing 2025.
81 PointPillars
This method makes use of Velodyne laser scans.
 code 58.65 % 77.10 % 51.92 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
82 ARPNET 58.20 % 74.21 % 52.13 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
83 LGFE 57.41 % 73.59 % 50.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
84 PCNet3D 57.28 % 74.15 % 51.02 % 0.05 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: PCNet3D: A Pillar Based Cascaded 3D Object Detection Model Using LiDAR Point Cloud. International Conference on Computer Vision and Image Processing 2024.
85 work6_new1 57.14 % 72.38 % 51.29 % 0.5 s GPU @ 2.5 Ghz (Python)
86 CS3D 56.92 % 74.30 % 50.41 % 0.5 s 1 core @ 2.5 Ghz (Python)
87 epBRM
This method makes use of Velodyne laser scans.
 code 56.13 % 72.08 % 49.91 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
88 F-PointNet
This method makes use of Velodyne laser scans.
 code 56.12 % 72.27 % 49.01 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
89 M3DNet 55.64 % 70.47 % 49.24 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
T. Prasanth, R. Padhy and B. Sivaselvan: M3DNet: Minimalist 3D Detection Backbone for Efficient and Accurate 3D Object Detection. Proceedings of the 18th International Conference on Agents and Artificial Intelligence (ICAART) 2026.
90 SeSame-point code 54.56 % 69.55 % 48.34 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
91 PointPillars_mmdet3d 54.47 % 70.51 % 48.34 % 0.03 s 1 core @ 2.5 Ghz (Python)
92 SeSame-voxel code 54.36 % 70.97 % 48.66 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
93 DSFNet 54.26 % 67.80 % 48.25 % 0.03 s GPU @ 2.5 Ghz (Python)
T. Prasanth, R. Padhy and B. Sivaselvan: LiDAR Sensor-Based Dual-Scale Fusion Approach for Bird’s-Eye View Sensing in Autonomous Vehicles. IEEE Sensors Letters 2025.
94 BirdNet+
This method makes use of Velodyne laser scans.
 code 53.84 % 65.67 % 49.06 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
95 BASE
This method uses stereo information.
This method makes use of Velodyne laser scans.
 53.73 % 71.57 % 48.92 % N/A s GPU @ 2.5 Ghz (Python)
96 GBC2DNet 53.64 % 69.18 % 47.52 % 0.05 s GPU @ 2.5 Ghz (Python)
97 DynaMo3D 52.80 % 66.32 % 47.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
98 PointRGBNet 52.15 % 67.05 % 46.78 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
99 SeSame-pillar code 51.74 % 64.55 % 46.13 % N/A s TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
100 M-VPFnet 51.50 % 68.26 % 45.05 % 0.05 s 1 core @ 2.5 Ghz (Python)
101 DMF
This method uses stereo information.
 51.33 % 65.51 % 45.05 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
102 PiFeNet code 51.10 % 67.50 % 44.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.
103 SCNet
This method makes use of Velodyne laser scans.
 50.79 % 67.98 % 45.15 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
104 AVOD-FPN
This method makes use of Velodyne laser scans.
 code 50.55 % 63.76 % 44.93 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
105 Fade 3D code 50.02 % 66.04 % 44.39 % 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.
106 MLOD
This method makes use of Velodyne laser scans.
 code 49.43 % 68.81 % 42.84 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
107 PL++: PV-RCNN++
This method uses stereo information.
This method makes use of Velodyne laser scans.
 48.97 % 62.80 % 42.80 % 0.342 s RTX 4060Ti (Python)
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.
108 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
 code 47.72 % 67.38 % 42.89 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
109 PFF3D
This method makes use of Velodyne laser scans.
 code 46.78 % 63.27 % 41.37 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
110 StereoDistill 44.02 % 63.96 % 39.19 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
111 DSGN++
This method uses stereo information.
 code 43.90 % 62.82 % 39.21 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
112 AVOD
This method makes use of Velodyne laser scans.
 code 42.08 % 57.19 % 38.29 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
113 SeSame-voxel w/score code 40.05 % 53.37 % 35.71 % N/A s GPU @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
114 SparsePool code 37.33 % 52.61 % 33.39 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
115 MMLAB LIGA-Stereo
This method uses stereo information.
 code 36.86 % 54.44 % 32.06 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
116 SparsePool code 32.61 % 40.87 % 29.05 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
117 CG-Stereo
This method uses stereo information.
 30.89 % 47.40 % 27.23 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
118 BirdNet
This method makes use of Velodyne laser scans.
 30.25 % 43.98 % 27.21 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
119 Disp R-CNN (velo)
This method uses stereo information.
 code 24.40 % 40.05 % 21.12 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
120 Disp R-CNN
This method uses stereo information.
 code 24.40 % 40.04 % 21.12 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
121 StereoDETR
This method uses stereo information.
 code 24.29 % 39.09 % 20.77 % 0.02 s GPU @ 2.5 Ghz (Python)
S. Mu, Z. Gu, Z. Ai, A. Liu, Y. Gao and S. Xu: StereoDETR: Stereo-based Transformer for 3D Object Detection. 2025.
122 DDStereo
This method uses stereo information.
 21.80 % 34.59 % 18.59 % 0.02 s GPU @ 2.5 Ghz (Python)
123 SG3D 20.16 % 34.82 % 17.63 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
124 Complexer-YOLO
This method makes use of Velodyne laser scans.
 18.53 % 24.27 % 17.31 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
125 DSGN
This method uses stereo information.
 code 18.17 % 27.76 % 16.21 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
126 OC Stereo
This method uses stereo information.
 code 16.63 % 29.40 % 14.72 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
127 SeSame-pillar w/scor code 14.29 % 11.47 % 12.57 % N/A s 1 core @ 2.5 Ghz (C/C++)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
128 RT3D-GMP
This method uses stereo information.
 12.99 % 18.31 % 10.63 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
129 EfficientStereo-3D 9.40 % 16.43 % 8.42 % 0.06 s 1 core @ 2.5 Ghz (Python)
130 SeSame-point w/score code 8.31 % 9.99 % 6.87 % N/A s 1 core @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
131 ESGN
This method uses stereo information.
 7.69 % 13.84 % 6.75 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
132 MonoPro 7.04 % 11.08 % 6.46 % 0.01 s 1 core @ 2.5 Ghz (Python)
133 CMKD code 6.67 % 12.52 % 6.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
134 PS-fld code 6.18 % 11.22 % 5.21 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
135 MonOri code 5.87 % 9.47 % 5.35 % 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.
136 MonoCtrl_MonoLSS 5.70 % 9.44 % 5.29 % 0.04 s 1 core @ 2.5 Ghz (Python)
137 LeAD-M3D 5.47 % 9.28 % 4.70 % 0.0079 s 1 core @ 2.5 Ghz (Python)
138 MonoLSPF 5.37 % 8.75 % 4.67 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
139 RARE 5.31 % 9.04 % 4.24 % 0.03 s 1 core @ 2.5 Ghz (Python)
140 MonoLiG code 5.24 % 8.14 % 4.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
141 DA3D+KM3D+v2-99 code 5.11 % 8.58 % 4.48 % 0.120s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
142 MonoCoP code 5.08 % 8.89 % 4.53 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhang, A. Kumar, G. Ganesan and X. Liu: Unleashing the Power of Chain-of- Prediction for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026.
143 Mix-Teaching code 4.91 % 8.04 % 4.15 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
144 fdaa11 4.79 % 8.03 % 4.13 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
145 DD3D code 4.79 % 7.52 % 4.22 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
146 MonoGeo code 4.78 % 8.08 % 3.96 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
147 MonoPSR code 4.74 % 8.37 % 3.68 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
148 PS-SVDM 4.57 % 7.98 % 3.66 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
149 TopNet-UncEst
This method makes use of Velodyne laser scans.
 4.54 % 7.13 % 3.81 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
150 CPD(unsupervised) code 4.43 % 6.75 % 3.84 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
H. Wu, S. Zhao, X. Huang, C. Wen, X. Li and C. Wang: Commonsense Prototype for Outdoor Unsupervised 3D Object Detection. CVPR 2024.
151 LPCG-Monoflex code 4.38 % 6.98 % 3.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
152 MonoLSS 4.34 % 7.23 % 3.92 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
153 MonoUNI code 4.28 % 7.34 % 3.78 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
154 MonoHPE 4.24 % 6.26 % 3.98 % 0.04 s 1 core @ 2.5 Ghz (Python)
155 Plane-Constraints code 4.22 % 7.72 % 3.36 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
156 IDEAL-M3D 4.12 % 6.93 % 3.71 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Meier, F. Günther, R. Marin, O. Dhaouadi, J. Kaiser and D. Cremers: IDEAL-M3D: Instance Diversity- Enriched Active Learning for Monocular 3D Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026.
157 MonoHPE-Mask 4.10 % 7.40 % 4.21 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
158 AMNet code 4.03 % 6.53 % 3.31 % 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.
159 Monodgc-3classes code 3.91 % 7.17 % 3.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
160 MonoDDE 3.78 % 5.94 % 3.33 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
161 DFR-Net 3.58 % 5.69 % 3.10 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
162 MonoCLUE 3.50 % 5.58 % 3.07 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
163 HomoLoss(monoflex) code 3.50 % 5.48 % 2.99 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
164 OPA-3D code 3.45 % 5.16 % 2.86 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
165 CaDDN code 3.41 % 7.00 % 3.30 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
166 RT3DStereo
This method uses stereo information.
 3.37 % 5.29 % 2.57 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
167 MonoFRD 3.33 % 6.38 % 3.12 % 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 MonoDTR 3.27 % 5.05 % 3.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
169 GUPNet code 3.21 % 5.58 % 2.66 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
170 MonoCLUE_all 3.20 % 5.93 % 2.94 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
171 DEVIANT code 3.13 % 5.05 % 2.59 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
172 CIE 3.09 % 5.62 % 2.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
173 SGM3D code 2.92 % 5.49 % 2.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
174 PS-SVDM 2.92 % 5.56 % 2.36 % 1 s 1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
175 AMNet+DDAD15M code 2.79 % 4.30 % 2.51 % 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.
176 MonOAPC 2.74 % 4.46 % 2.14 % 0035 s 1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
177 MDSNet 2.68 % 5.37 % 2.22 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
178 Cube R-CNN code 2.67 % 3.65 % 2.28 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
179 temp 2.67 % 4.70 % 2.36 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
180 monodle code 2.66 % 4.59 % 2.45 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
181 DDMP-3D 2.50 % 4.18 % 2.32 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
182 MonoNeRD code 2.48 % 4.73 % 2.16 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
183 Aug3D-RPN 2.43 % 4.36 % 2.55 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
184 QD-3DT
This is an online method (no batch processing).
 code 2.39 % 4.16 % 1.85 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
185 MonoFlex 2.35 % 4.17 % 2.04 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
186 MonoPair 2.12 % 3.79 % 1.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
187 DA3D code 1.86 % 3.37 % 1.48 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
188 RefinedMPL 1.82 % 3.23 % 1.77 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
189 MonoRCNN++ code 1.81 % 3.17 % 1.75 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
190 TopNet-HighRes
This method makes use of Velodyne laser scans.
 1.67 % 2.49 % 1.88 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
191 D4LCN code 1.67 % 2.45 % 1.36 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
192 FMF-occlusion-net 1.60 % 1.87 % 1.66 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
193 SS3D 1.45 % 2.80 % 1.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
194 PGD-FCOS3D code 1.38 % 2.81 % 1.20 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
195 DA3D+KM3D code 1.37 % 2.79 % 1.32 % 0.02 s GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
196 CMAN 1.05 % 1.59 % 1.11 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
197 MonoEF 0.92 % 1.80 % 0.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
198 M3D-RPN code 0.65 % 0.94 % 0.47 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
199 MonoRUn code 0.61 % 1.01 % 0.48 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
200 Shift R-CNN (mono) code 0.29 % 0.48 % 0.31 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
201 tmp 0.00 % 0.00 % 0.00 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
202 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

Related Datasets

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}
}



eXTReMe Tracker