Bird's Eye View Evaluation 2017


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

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

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 VirConv-S code 93.52 % 95.99 % 90.38 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
2 UDeerPEP code 93.40 % 95.34 % 89.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Z. Dong, H. Ji, X. Huang, W. Zhang, X. Zhan and J. Chen: PeP: a Point enhanced Painting method for unified point cloud tasks. 2023.
3 VirConv-T code 92.65 % 96.11 % 89.69 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
4 GraR-Po code 92.12 % 95.79 % 87.11 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
5 TSSTDet 92.11 % 95.80 % 89.23 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang, D. Bui and M. Yoo: TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer. IEEE Sensors Journal 2024.
6 TED code 92.05 % 95.44 % 87.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
7 MB3D 91.93 % 95.33 % 88.71 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
8 PVFusion code 91.87 % 95.01 % 86.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
9 VPFNet code 91.86 % 93.02 % 86.94 % 0.06 s 2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. IEEE Transactions on Multimedia 2022.
10 SFD code 91.85 % 95.64 % 86.83 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
11 SE-SSD
This method makes use of Velodyne laser scans.
code 91.84 % 95.68 % 86.72 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
12 HDet3D 91.82 % 94.90 % 84.68 % 0.07 s >8 cores @ 2.5 Ghz (Python)
13 LVP(84.92) 91.80 % 95.49 % 88.91 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
14 ACFNet 91.78 % 92.91 % 87.06 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
15 GraR-Vo code 91.72 % 95.27 % 86.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
16 NIV-SSD 91.69 % 95.66 % 86.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
17 CDF 91.67 % 92.68 % 86.62 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
18 PVT-SSD 91.63 % 95.23 % 86.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He and W. Ouyang: PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer. CVPR 2023.
19 HAF-PVP_test 91.60 % 95.33 % 86.71 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
20 SPANet 91.59 % 95.59 % 86.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
21 CasA code 91.54 % 95.19 % 86.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
22 FEIF3D
This method makes use of Velodyne laser scans.
91.53 % 95.29 % 86.87 % 0.1 s GPU @ 2.5 Ghz (Python)
23 LoGoNet code 91.52 % 95.48 % 87.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
24 GraR-Pi code 91.52 % 95.06 % 86.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
25 MuTOD 91.51 % 95.69 % 88.71 % 0.04 s 1 core @ 2.5 Ghz (Python)
26 MAK_VOXEL_RCNN 91.46 % 95.32 % 86.81 % 0.03 s 1 core @ 2.5 Ghz (Python)
27 UPIDet code 91.36 % 92.96 % 86.80 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
28 BADet code 91.32 % 95.23 % 86.48 % 0.14 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.
29 Anonymous 91.30 % 95.26 % 86.73 % 0.1 s 1 core @ 2.5 Ghz (Python)
30 ANM code 91.30 % 94.91 % 88.51 % ANM ANM
31 LVP 91.29 % 94.53 % 86.48 % 0.04 s 1 core @ 2.5 Ghz (Python)
32 VDF 91.28 % 94.80 % 86.43 % 0.03 s GPU @ 2.5 Ghz (Python)
33 DEF-Model 91.28 % 93.03 % 86.48 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
34 SDGUFusion 91.26 % 94.86 % 88.55 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
35 TED-S Reproduced 91.23 % 95.34 % 86.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
36 SFD++ 91.22 % 94.67 % 86.28 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
37 URFormer 91.22 % 94.40 % 86.35 % 0.1 s 1 core @ 2.5 Ghz (Python)
38 CasA++ code 91.22 % 94.57 % 88.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
39 OGMMDet code 91.21 % 95.59 % 88.33 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
40 RPF3D 91.18 % 92.94 % 86.58 % 0.1 s 1 core @ 2.5 Ghz (Python)
41 voxel_spark code 91.18 % 94.82 % 86.58 % 0.04 s GPU @ 2.5 Ghz (C/C++)
42 spark 91.13 % 94.93 % 86.54 % 0.1 s 1 core @ 2.5 Ghz (Python)
43 3D HANet code 91.13 % 94.33 % 86.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, Y. Chen, G. Cai, G. Chen, D. Xie, J. Su and Z. Wang: 3D HANet: A Flexible 3D Heatmap Auxiliary Network for Object Detection. IEEE Transactions on Geoscience and Remote Sensing 2023.
44 test 91.12 % 93.93 % 86.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 DiffCandiDet 91.11 % 95.05 % 86.45 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
46 spark_voxel_rcnn code 91.08 % 94.61 % 86.59 % 0.04 s 1 core @ 2.5 Ghz (Python)
47 TEDx 91.05 % 92.74 % 88.17 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
48 SA-SSD code 91.03 % 95.03 % 85.96 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
49 L-AUG 91.00 % 94.52 % 88.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
50 TED_S_baseline code 90.98 % 94.56 % 86.41 % 0.09 s 1 core @ 2.5 Ghz (Python)
51 spark2 90.95 % 92.93 % 86.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 3D-BCM 90.95 % 94.64 % 86.16 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 Voxel_Spark_focal_we code 90.93 % 94.83 % 86.45 % 0.08 s 1 core @ 2.5 Ghz (Python)
54 SGFNet 90.91 % 94.01 % 86.12 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
55 3D Dual-Fusion code 90.86 % 93.08 % 86.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
Y. Kim, K. Park, M. Kim, D. Kum and J. Choi: 3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection. arXiv preprint arXiv:2211.13529 2022.
56 LGSLNet 90.85 % 94.35 % 88.27 % 0.1 s GPU @ 2.5 Ghz (Python)
57 PR-SSD 90.78 % 94.23 % 86.14 % 0.02 s GPU @ 2.5 Ghz (Python)
58 GraphAlign(ICCV2023) code 90.73 % 94.46 % 88.34 % 0.03 s GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
59 ECA 90.70 % 94.10 % 86.04 % 0.08 s GPU @ 1.5 Ghz (Python)
60 GF-pointnet 90.67 % 93.88 % 86.09 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
61 SDGUFusion 90.65 % 95.10 % 86.45 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
62 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 90.65 % 94.98 % 86.14 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
63 SQD 90.63 % 95.44 % 88.04 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
64 focalnet 90.61 % 94.46 % 88.12 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
65 BPG3D 90.57 % 93.00 % 86.21 % 0.05 s 1 core @ 2.5 Ghz (Python)
66 focalnet 90.56 % 94.52 % 88.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
67 VPFNet code 90.52 % 93.94 % 86.25 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
68 PDV code 90.48 % 94.56 % 86.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
69 LGNet-3classes code 90.44 % 94.98 % 86.06 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
70 Spark_PartA2_Soft_fo code 90.38 % 93.90 % 85.91 % 0.1 s 1 core @ 2.5 Ghz (Python)
71 M3DeTR code 90.37 % 94.41 % 85.98 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
72 VoTr-TSD code 90.34 % 94.03 % 86.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
73 HA-PillarNet 90.33 % 94.29 % 85.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
74 MSIT-Det 90.24 % 96.01 % 80.53 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
75 Spark_partA22 90.23 % 92.61 % 85.89 % 10 s 1 core @ 2.5 Ghz (Python)
76 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 90.13 % 92.42 % 85.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
77 LFT 90.12 % 95.83 % 85.06 % 0.1s 1 core @ 2.5 Ghz (C/C++)
78 XView 90.12 % 92.27 % 85.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
79 SFA-GCL code 90.12 % 95.75 % 84.97 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
80 SFA-GCL(80) code 90.11 % 95.76 % 84.96 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
81 GraR-VoI code 90.10 % 95.69 % 86.85 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
82 CAT-Det 90.07 % 92.59 % 85.82 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
83 3ONet 90.07 % 95.87 % 85.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
84 SFA-GCL(80, k=4) code 90.04 % 95.67 % 84.91 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
85 focal 90.02 % 94.56 % 85.95 % 100 s 1 core @ 2.5 Ghz (Python)
86 GeVo 90.02 % 92.45 % 85.86 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
87 spark-part2 90.01 % 93.82 % 85.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
88 IMLIDAR(base) 89.93 % 93.57 % 85.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
89 CG-SSD 89.93 % 94.26 % 85.76 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
90 Anonymous code 89.91 % 93.38 % 84.91 % 0.04 s 1 core @ 2.5 Ghz (Python)
91 OFFNet 89.88 % 91.62 % 85.57 % 0.1 s GPU @ 2.5 Ghz (Python)
92 SVGA-Net 89.88 % 92.07 % 85.59 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
93 DDF 89.87 % 93.46 % 84.92 % 0.1 s 1 core @ 2.5 Ghz (Python)
94 EBM3DOD code 89.86 % 95.64 % 84.56 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
95 U_PV_V2_ep_100_100 89.86 % 92.17 % 85.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
96 CIA-SSD
This method makes use of Velodyne laser scans.
code 89.84 % 93.74 % 82.39 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
97 MLF-DET 89.82 % 93.38 % 84.78 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
98 CLOCs_PVCas code 89.80 % 93.05 % 86.57 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
99 VoxelFSD 89.79 % 92.57 % 85.77 % 0.08 s 1 core @ 2.5 Ghz (Python)
100 GLENet-VR code 89.76 % 93.48 % 84.89 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D object detectors with generative label uncertainty estimation. International Journal of Computer Vision 2023.
Y. Zhang, J. Hou and Y. Yuan: A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks. International Journal of Computer Vision 2023.
101 RDIoU code 89.75 % 94.90 % 84.67 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single- stage 3D Object Detection. ECCV 2022.
102 PV-RCNN-Plus 89.75 % 91.93 % 85.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
103 SFA-GCL(baseline) code 89.74 % 95.55 % 84.63 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
104 SFA-GCL_dataaug code 89.73 % 93.44 % 84.60 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
105 fuf 89.72 % 95.47 % 84.59 % 10 s 1 core @ 2.5 Ghz (C/C++)
106 SFA-GCL code 89.71 % 93.53 % 84.58 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
107 DGEnhCL code 89.66 % 95.21 % 84.53 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
108 EBM3DOD baseline code 89.63 % 95.44 % 84.34 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
109 PIPC-3Ddet code 89.62 % 93.40 % 84.65 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
110 VPA 89.61 % 95.46 % 86.81 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
111 MAK code 89.59 % 93.21 % 86.84 % 0.03 s GPU @ 2.5 Ghz (Python)
112 casx 89.58 % 93.39 % 84.75 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
113 pointpillars_spark code 89.57 % 92.98 % 84.91 % 0.02 s GPU @ 2.5 Ghz (C/C++)
114 3D-CVF at SPA
This method makes use of Velodyne laser scans.
code 89.56 % 93.52 % 82.45 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
115 OcTr 89.56 % 93.08 % 86.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
C. Zhou, Y. Zhang, J. Chen and D. Huang: OcTr: Octree-based Transformer for 3D Object Detection. CVPR 2023.
116 Struc info fusion II 89.54 % 95.26 % 82.31 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
117 spark_second_focal_w 89.53 % 91.19 % 85.11 % 0.1 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
118 spark_second code 89.53 % 91.23 % 85.02 % . s 1 core @ 2.5 Ghz (Python)
119 PSMS-Net
This method makes use of Velodyne laser scans.
89.53 % 95.32 % 86.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 IIOU code 89.52 % 92.90 % 84.56 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
121 spark_pointpillar code 89.51 % 93.58 % 85.03 % 0.02 s GPU @ 2.5 Ghz (Python)
122 SASA
This method makes use of Velodyne laser scans.
code 89.51 % 92.87 % 86.35 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
123 Fast-CLOCs 89.49 % 93.03 % 86.40 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
124 IA-SSD (single) code 89.48 % 93.14 % 84.42 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
125 KPTr 89.48 % 92.74 % 84.50 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
126 CLOCs code 89.48 % 92.91 % 86.42 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
127 PA3DNet 89.46 % 93.11 % 84.60 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
M. Wang, L. Zhao and Y. Yue: PA3DNet: 3-D Vehicle Detection with Pseudo Shape Segmentation and Adaptive Camera- LiDAR Fusion. IEEE Transactions on Industrial Informatics 2023.
128 PG-RCNN code 89.46 % 93.39 % 86.54 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
129 DFAF3D 89.45 % 93.14 % 84.22 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
130 DVF-V 89.42 % 93.12 % 86.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
131 Struc info fusion I 89.38 % 94.91 % 84.29 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
132 DA-Net 89.34 % 94.97 % 86.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
133 BtcDet
This method makes use of Velodyne laser scans.
code 89.34 % 92.81 % 84.55 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
134 casxv1 89.33 % 92.88 % 84.55 % 0.01 s 1 core @ 2.5 Ghz (Python)
135 IA-SSD (multi) code 89.33 % 92.79 % 84.35 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
136 SS-3DSSD code 89.27 % 92.61 % 86.10 % 0.014s 1 core @ 2.5 Ghz (C/C++)
137 spark_second2 89.27 % 90.94 % 84.85 % 10 s 1 core @ 2.5 Ghz (Python)
138 ACDet code 89.21 % 92.87 % 85.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
139 DVF-PV 89.20 % 93.08 % 86.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
140 Test_dif code 89.20 % 92.69 % 84.23 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
141 STD code 89.19 % 94.74 % 86.42 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
142 FIRM-Net 89.18 % 92.56 % 86.33 % 0.07 s 1 core @ 2.5 Ghz (Python)
143 Point-GNN
This method makes use of Velodyne laser scans.
code 89.17 % 93.11 % 83.90 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
144 HMFI code 89.17 % 93.04 % 86.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
145 sec_spark code 89.16 % 90.89 % 84.84 % 0.03 s GPU @ 2.5 Ghz (Python)
146 SSL-PointGNN code 89.16 % 92.92 % 83.99 % 0.56 s GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
147 MMAE
This method makes use of Velodyne laser scans.
89.15 % 92.42 % 84.01 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
148 SPG_mini
This method makes use of Velodyne laser scans.
code 89.12 % 92.80 % 86.27 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
149 PA-Det3D 89.11 % 92.40 % 86.51 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
150 EQ-PVRCNN code 89.09 % 94.55 % 86.42 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
151 VoxSeT code 89.07 % 92.70 % 86.29 % 33 ms 1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
152 RAFDet 89.07 % 92.64 % 85.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
153 RAFDet 89.05 % 92.29 % 84.35 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
154 3DSSD code 89.02 % 92.66 % 85.86 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
155 EPNet++ 89.00 % 95.41 % 85.73 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
156 IOUFusion 89.00 % 92.47 % 84.10 % 0.1 s GPU @ 2.5 Ghz (Python)
157 Focals Conv code 89.00 % 92.67 % 86.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
158 RAFDet code 88.99 % 92.23 % 84.21 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
159 LGNet-Car code 88.98 % 92.83 % 86.26 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
160 CAFI-Pillars 88.96 % 92.32 % 86.24 % 30ms NVIDIA Tesla P40 GPU
161 USVLab BSAODet code 88.90 % 92.66 % 86.23 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
162 bs 88.88 % 94.53 % 86.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
163 CZY_PPF_Net 88.88 % 94.68 % 86.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
164 H^23D R-CNN code 88.87 % 92.85 % 86.07 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
165 Pyramid R-CNN 88.84 % 92.19 % 86.21 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
166 CityBrainLab-CT3D code 88.83 % 92.36 % 84.07 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
167 Voxel R-CNN code 88.83 % 94.85 % 86.13 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
168 HVNet 88.82 % 92.83 % 83.38 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
169 AMVFNet code 88.82 % 92.68 % 86.18 % 0.04 s GPU @ 2.5 Ghz (Python)
170 GD-MAE 88.82 % 94.22 % 83.54 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, T. He, J. Liu, H. Chen, B. Wu, B. Lin, X. He and W. Ouyang: GD-MAE: Generative Decoder for MAE Pre- training on LiDAR Point Clouds. CVPR 2023.
171 Under Blind Review#1 88.77 % 92.10 % 86.06 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
172 SPG
This method makes use of Velodyne laser scans.
code 88.70 % 94.33 % 85.98 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
173 F3D 88.70 % 92.26 % 85.84 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
174 MG 88.66 % 92.64 % 83.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
175 SIENet code 88.65 % 92.38 % 86.03 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
176 P2V-RCNN 88.63 % 92.72 % 86.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
177 FromVoxelToPoint code 88.61 % 92.23 % 86.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
178 RangeIoUDet
This method makes use of Velodyne laser scans.
88.59 % 92.28 % 85.83 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
179 af 88.58 % 92.43 % 86.05 % 1 s GPU @ 2.5 Ghz (Python)
180 GA-RCNN 88.56 % 92.17 % 85.95 % 47ms 1 core @ 2.5 Ghz (C/C++)
181 RBEV-Voxel code 88.54 % 91.92 % 85.73 % 0.08 s GPU @ 2.5 Ghz (Python)
182 EPNet code 88.47 % 94.22 % 83.69 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
183 CenterNet3D 88.46 % 91.80 % 83.62 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
184 FARP-Net code 88.45 % 91.20 % 86.01 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
T. Xie, L. Wang, K. Wang, R. Li, X. Zhang, H. Zhang, L. Yang, H. Liu and J. Li: FARP-Net: Local-Global Feature Aggregation and Relation-Aware Proposals for 3D Object Detection. IEEE Transactions on Multimedia 2023.
185 PUDet 88.42 % 92.68 % 83.70 % 0.3 s GPU @ 2.5 Ghz (Python)
186 RangeRCNN
This method makes use of Velodyne laser scans.
88.40 % 92.15 % 85.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
187 Patches
This method makes use of Velodyne laser scans.
88.39 % 92.72 % 83.19 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
188 3D IoU-Net 88.38 % 94.76 % 81.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
189 StructuralIF 88.38 % 91.78 % 85.67 % 0.02 s 8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
190 PASS-PV-RCNN-Plus 88.37 % 92.17 % 85.75 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
191 AAMVFNet code 88.36 % 92.31 % 85.81 % 0.04 s GPU @ 2.5 Ghz (Python)
192 U_PV_V2_ep100_80 88.25 % 91.88 % 85.67 % 0... s 1 core @ 2.5 Ghz (Python)
193 CLOCs_SecCas 88.23 % 91.16 % 82.63 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
194 UberATG-MMF
This method makes use of Velodyne laser scans.
88.21 % 93.67 % 81.99 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
195 MMpointpillars 88.17 % 91.76 % 81.13 % 0.05 s 1 core @ 2.5 Ghz (Python)
196 Patches - EMP
This method makes use of Velodyne laser scans.
88.17 % 94.49 % 84.75 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
197 SRDL 88.17 % 92.01 % 85.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
198 P2P code 88.15 % 91.92 % 81.12 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
199 PointPainting
This method makes use of Velodyne laser scans.
88.11 % 92.45 % 83.36 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
200 SERCNN
This method makes use of Velodyne laser scans.
88.10 % 94.11 % 83.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
201 PartA2_basline code 88.09 % 92.35 % 85.42 % 0.09 s 1 core @ 2.5 Ghz (Python)
202 Associate-3Ddet code 88.09 % 91.40 % 82.96 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
203 HotSpotNet 88.09 % 94.06 % 83.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
204 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 88.08 % 91.90 % 85.35 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
205 SFEBEV 88.08 % 93.44 % 83.01 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
206 pointpillar_spark_fo 88.02 % 92.48 % 84.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
207 UberATG-HDNET
This method makes use of Velodyne laser scans.
87.98 % 93.13 % 81.23 % 0.05 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
208 spark_pointpillar2 87.93 % 92.74 % 84.70 % 10 s 1 core @ 2.5 Ghz (Python)
209 BAPartA2S-4h 87.89 % 91.96 % 83.31 % 0.1 s 1 core @ 2.5 Ghz (Python)
210 Fast Point R-CNN
This method makes use of Velodyne laser scans.
87.84 % 90.87 % 80.52 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
211 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 87.79 % 91.70 % 84.61 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
212 SIF 87.76 % 91.44 % 85.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
213 MVAF-Net code 87.73 % 91.95 % 85.00 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
214 DVFENet 87.68 % 90.93 % 84.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
215 S-AT GCN 87.68 % 90.85 % 84.20 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
216 RangeDet (Official) code 87.67 % 90.93 % 82.92 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
217 pointpillar_baseline code 87.61 % 92.52 % 83.84 % 0.01 s 1 core @ 2.5 Ghz (Python)
218 Second_baseline code 87.60 % 90.94 % 84.36 % 0.03 s 1 core @ 2.5 Ghz (Python)
219 VoxelFSD-S 87.60 % 90.94 % 84.11 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
220 U_second_v4_ep_100_8 87.58 % 90.54 % 84.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
221 SC-SSD 87.56 % 90.70 % 84.36 % 1 s 1 core @ 2.5 Ghz (C/C++)
222 MODet
This method makes use of Velodyne laser scans.
87.56 % 90.80 % 82.69 % 0.05 s GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
223 TF-PartA2 87.54 % 91.93 % 83.33 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
224 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 87.53 % 91.99 % 81.03 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
225 mm3d_PartA2 87.51 % 91.75 % 83.01 % 0.1 s GPU @ >3.5 Ghz (Python)
226 SeSame-point code 87.49 % 90.84 % 83.77 % N/A s TITAN RTX @ 1.35 Ghz (Python)
227 PointRGCN 87.49 % 91.63 % 80.73 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
228 MGAF-3DSSD code 87.47 % 92.70 % 82.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
229 PC-CNN-V2
This method makes use of Velodyne laser scans.
87.40 % 91.19 % 79.35 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
230 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 87.39 % 92.13 % 82.72 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
231 Sem-Aug
This method makes use of Velodyne laser scans.
87.37 % 93.35 % 82.43 % 0.1 s GPU @ 2.5 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.
232 MAFF-Net(DAF-Pillar) 87.34 % 90.79 % 77.66 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
233 Harmonic PointPillar code 87.28 % 90.89 % 82.54 % 0.01 s 1 core @ 2.5 Ghz (Python)
H. Zhang, J. Mekala, Z. Nain, J. Park and H. Jung: 3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection for V2X Orchestration. will submit to IEEE Transactions on Vehicular Technology 2022.
234 u_second_v4_epoch_10 87.23 % 89.67 % 84.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
235 PASS-PointPillar 87.23 % 91.07 % 81.98 % 1 s 1 core @ 2.5 Ghz (C/C++)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
236 HRI-VoxelFPN 87.21 % 92.75 % 79.82 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
237 PVTr 87.17 % 91.25 % 84.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
238 epBRM
This method makes use of Velodyne laser scans.
code 87.13 % 90.70 % 81.92 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
239 LVFSD 87.12 % 90.42 % 83.91 % 0.06 s
ERROR: Wrong syntax in BIBTEX file.
240 XT-PartA2 87.08 % 90.89 % 82.70 % 0.1 s GPU @ >3.5 Ghz (Python)
241 centerpoint_pcdet 87.04 % 90.04 % 83.32 % 0.06 s 1 core @ 2.5 Ghz (Python)
242 SARPNET 86.92 % 92.21 % 81.68 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
243 SeSame-pillar code 86.88 % 90.61 % 81.93 % N/A s TITAN RTX @ 1.35 Ghz (Python)
244 ARPNET 86.81 % 90.06 % 79.41 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
245 C-GCN 86.78 % 91.11 % 80.09 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
246 PointPillars
This method makes use of Velodyne laser scans.
code 86.56 % 90.07 % 82.81 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
247 TANet code 86.54 % 91.58 % 81.19 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
248 MEDL-U 86.50 % 91.75 % 79.29 % 1 s GPU @ >3.5 Ghz (Python)
249 SCNet
This method makes use of Velodyne laser scans.
86.48 % 90.07 % 81.30 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
250 MM_SECOND code 86.39 % 90.52 % 81.49 % 0.05 s GPU @ >3.5 Ghz (Python)
251 SegVoxelNet 86.37 % 91.62 % 83.04 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
252 IIOU_LDR code 86.31 % 91.80 % 81.30 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
253 3D IoU Loss
This method makes use of Velodyne laser scans.
86.22 % 91.36 % 81.20 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
254 VSAC 86.22 % 91.98 % 81.50 % 0.07 s 1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
255 prcnn_v18_80_100 86.20 % 90.79 % 81.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
256 voxelnext_pcdet 86.15 % 89.72 % 82.34 % 0.05 s 1 core @ 2.5 Ghz (Python)
257 ROT_S3D 86.11 % 91.33 % 81.17 % 0.1 s GPU @ 2.5 Ghz (Python)
258 SeSame-pillar w/scor code 86.11 % 90.43 % 81.38 % N/A s 1 core @ 2.5 Ghz (C/C++)
259 R-GCN 86.05 % 91.91 % 81.05 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
260 UberATG-PIXOR++
This method makes use of Velodyne laser scans.
86.01 % 93.28 % 80.11 % 0.035 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
261 HINTED 86.01 % 90.61 % 79.29 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
262 MMpp 85.97 % 91.28 % 80.82 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
263 CAT2 85.90 % 91.13 % 80.81 % 1 s 1 core @ 2.5 Ghz (C/C++)
264 Sem-Aug-PointRCNN++ 85.88 % 91.68 % 83.37 % 0.1 s 8 cores @ 3.0 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.
265 DASS 85.85 % 91.74 % 80.97 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
266 F-ConvNet
This method makes use of Velodyne laser scans.
code 85.84 % 91.51 % 76.11 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
267 PI-RCNN 85.81 % 91.44 % 81.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
268 PointRGBNet 85.73 % 91.39 % 80.68 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
269 HA PillarNet 85.69 % 90.43 % 80.75 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
270 SeSame-voxel code 85.62 % 89.86 % 80.95 % N/A s TITAN RTX @ 1.35 Ghz (Python)
271 WA 85.61 % 90.76 % 79.99 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
272 UberATG-ContFuse
This method makes use of Velodyne laser scans.
85.35 % 94.07 % 75.88 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
273 PFF3D
This method makes use of Velodyne laser scans.
code 85.08 % 89.61 % 80.42 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
274 AVOD
This method makes use of Velodyne laser scans.
code 84.95 % 89.75 % 78.32 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
275 WS3D
This method makes use of Velodyne laser scans.
84.93 % 90.96 % 77.96 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
276 PI-SECOND code 84.83 % 90.15 % 79.86 % 0.05 s GPU @ >3.5 Ghz (Python + C/C++)
277 ODGS 84.82 % 89.59 % 78.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
278 AVOD-FPN
This method makes use of Velodyne laser scans.
code 84.82 % 90.99 % 79.62 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
279 F-PointNet
This method makes use of Velodyne laser scans.
code 84.67 % 91.17 % 74.77 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
280 mmFUSION code 84.60 % 90.35 % 79.82 % 1s 1 core @ 2.5 Ghz (Python)
J. Ahmad and A. Del Bue: mmFUSION: Multimodal Fusion for 3D Objects Detection. arXiv preprint arXiv:2311.04058 2023.
281 3DBN
This method makes use of Velodyne laser scans.
83.94 % 89.66 % 76.50 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
282 MVAF-Net(3-classes) 83.81 % 90.05 % 77.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
283 EOTL code 83.14 % 89.10 % 71.41 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
284 DFSemONet(Baseline) 82.80 % 90.00 % 77.90 % 0.04 s GPU @ 2.5 Ghz (Python)
285 MLOD
This method makes use of Velodyne laser scans.
code 82.68 % 90.25 % 77.97 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
286 MVAF-Net(3-classes) 82.32 % 90.67 % 77.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
287 BirdNet+
This method makes use of Velodyne laser scans.
code 81.85 % 87.43 % 75.36 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
288 DMF
This method uses stereo information.
80.29 % 84.64 % 76.05 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
289 UberATG-PIXOR
This method makes use of Velodyne laser scans.
80.01 % 83.97 % 74.31 % 0.035 s TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
290 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
78.98 % 86.49 % 72.23 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
291 DSGN++
This method uses stereo information.
code 78.94 % 88.55 % 69.74 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
292 MV3D
This method makes use of Velodyne laser scans.
78.93 % 86.62 % 69.80 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
293 StereoDistill 78.59 % 89.03 % 69.34 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
294 MMLAB LIGA-Stereo
This method uses stereo information.
code 76.78 % 88.15 % 67.40 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
295 RCD 75.83 % 82.26 % 69.61 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
296 LaserNet 74.52 % 79.19 % 68.45 % 12 ms GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
297 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 73.80 % 84.61 % 65.59 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
298 SNVC
This method uses stereo information.
code 73.61 % 86.88 % 64.49 % 1 s GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
299 A3DODWTDA
This method makes use of Velodyne laser scans.
code 73.26 % 79.58 % 62.77 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
300 Complexer-YOLO
This method makes use of Velodyne laser scans.
68.96 % 77.24 % 64.95 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
301 TopNet-Retina
This method makes use of Velodyne laser scans.
68.16 % 80.16 % 63.43 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
302 SeSame-point w/score code 67.18 % 83.44 % 57.68 % N/A s GPU @ 1.5 Ghz (Python)
303 CG-Stereo
This method uses stereo information.
66.44 % 85.29 % 58.95 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
304 PLUME
This method uses stereo information.
66.27 % 82.97 % 56.70 % 0.15 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Yang, R. Hu, M. Liang and R. Urtasun: PLUME: Efficient 3D Object Detection from Stereo Images. IROS 2021.
305 CDN
This method uses stereo information.
code 66.24 % 83.32 % 57.65 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
306 DSGN
This method uses stereo information.
code 65.05 % 82.90 % 56.60 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
307 TopNet-DecayRate
This method makes use of Velodyne laser scans.
64.60 % 79.74 % 58.04 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
308 SeSame-voxel w/score code 63.36 % 71.98 % 57.52 % N/A s GPU @ 1.5 Ghz (Python)
309 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 63.33 % 84.80 % 61.23 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
310 3D FCN
This method makes use of Velodyne laser scans.
61.67 % 70.62 % 55.61 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
311 CDN-PL++
This method uses stereo information.
61.04 % 81.27 % 52.84 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
312 BirdNet
This method makes use of Velodyne laser scans.
59.83 % 84.17 % 57.35 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
313 TopNet-UncEst
This method makes use of Velodyne laser scans.
59.67 % 72.05 % 51.67 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
314 RT3D-GMP
This method uses stereo information.
59.00 % 69.14 % 45.49 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
315 Disp R-CNN (velo)
This method uses stereo information.
code 58.62 % 79.76 % 47.73 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
316 ESGN
This method uses stereo information.
58.12 % 78.10 % 49.28 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
317 Pseudo-LiDAR++
This method uses stereo information.
code 58.01 % 78.31 % 51.25 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
318 Disp R-CNN
This method uses stereo information.
code 57.98 % 79.61 % 47.09 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
319 ZoomNet
This method uses stereo information.
code 54.91 % 72.94 % 44.14 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
320 VoxelJones code 53.96 % 66.21 % 47.66 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
321 TopNet-HighRes
This method makes use of Velodyne laser scans.
53.05 % 67.84 % 46.99 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
322 OC Stereo
This method uses stereo information.
code 51.47 % 68.89 % 42.97 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
323 YOLOStereo3D
This method uses stereo information.
code 50.28 % 76.10 % 36.86 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
324 TS3D
This method uses stereo information.
48.59 % 73.34 % 36.98 % 0.09 s GPU @ 1.5 Ghz (Python + C/C++)
325 SST [st]
This method uses stereo information.
47.07 % 71.08 % 41.90 % 1 s 1 core @ 2.5 Ghz (Python)
326 RT3DStereo
This method uses stereo information.
46.82 % 58.81 % 38.38 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
327 Pseudo-Lidar
This method uses stereo information.
code 45.00 % 67.30 % 38.40 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
328 BKDStereo3D code 44.02 % 70.19 % 32.78 % 0.1 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
329 RT3D
This method makes use of Velodyne laser scans.
44.00 % 56.44 % 42.34 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
330 Stereo CenterNet
This method uses stereo information.
42.12 % 62.97 % 35.37 % 0.04 s GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
331 Stereo R-CNN
This method uses stereo information.
code 41.31 % 61.92 % 33.42 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
332 BKDStereo3D w/o KD code 40.69 % 67.38 % 29.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
333 MonoTRKDv2 36.89 % 46.87 % 33.39 % 40 s 1 core @ 2.5 Ghz (Python)
334 DA3D+KM3D+v2-99 34.88 % 44.27 % 30.29 % 0.120s GPU @ 2.5 Ghz (Python)
335 CIE + DM3D 33.13 % 46.17 % 28.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Ananimities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
336 StereoFENet
This method uses stereo information.
32.96 % 49.29 % 25.90 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
337 MonoTAKD V2 32.31 % 43.83 % 28.48 % 0.1 s 1 core @ 2.5 Ghz (Python)
338 SVDM-VIEW 30.78 % 49.96 % 26.51 % 1 s 1 core @ 2.5 Ghz (Python)
339 Mobile Stereo R-CNN
This method uses stereo information.
28.78 % 44.51 % 22.30 % 1.8 s NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
340 DA3D+KM3D 28.71 % 39.50 % 25.20 % 0.02 s GPU @ 2.5 Ghz (Python)
341 CIE 28.50 % 41.41 % 23.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
342 MonoTAKD 27.76 % 38.75 % 24.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
343 MonoLTKD 27.76 % 38.75 % 24.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
344 MonoLTKD_V3 27.75 % 38.75 % 24.13 % 0.04 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
345 DA3D 26.92 % 36.83 % 23.41 % 0.03 s 1 core @ 2.5 Ghz (Python)
346 MonoLiG code 26.83 % 35.73 % 24.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
347 MonoLSS 25.95 % 34.89 % 22.59 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
348 CMKD code 25.82 % 38.98 % 22.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
349 SH3D 25.25 % 35.64 % 22.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
350 LPCG-Monoflex code 24.81 % 35.96 % 21.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
351 Anonymous 24.66 % 34.65 % 20.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
352 NeurOCS 24.49 % 37.27 % 20.89 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Min, B. Zhuang, S. Schulter, B. Liu, E. Dunn and M. Chandraker: NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization. CVPR 2023.
353 Mix-Teaching code 24.23 % 35.74 % 20.80 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
354 MonoInsight 24.23 % 34.85 % 20.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
355 MonoInsight 24.23 % 34.85 % 20.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
356 MonoAux-v2 code 24.15 % 34.14 % 20.84 % 0.04 s GPU @ 2.5 Ghz (Python)
357 MonoSample (DID-M3D) 23.94 % 37.64 % 20.46 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
358 TBD 23.87 % 37.10 % 20.24 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
359 PS-fld code 23.76 % 32.64 % 20.64 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
360 MSFENet code 23.65 % 36.81 % 20.06 % 0.1 s 1 core @ 2.5 Ghz (Python)
361 SHUD 23.63 % 36.39 % 20.01 % 0.04 s 1 core @ 2.5 Ghz (Python)
362 ADD code 23.58 % 35.20 % 20.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Wu, Y. Wu, J. Pu, X. Li and X. Wang: Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection. AAAI2023 .
363 MonoNeRD code 23.46 % 31.13 % 20.97 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
364 MonoDDE 23.46 % 33.58 % 20.37 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
365 DD3D code 23.41 % 32.35 % 20.42 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
366 MOPNet code 23.33 % 36.40 % 19.70 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
367 MonoSGC 23.27 % 35.78 % 19.92 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
368 FDGNet code 23.27 % 36.25 % 19.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
369 MonoUNI code 23.05 % 33.28 % 19.39 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
370 MonoAux 23.00 % 32.30 % 19.84 % 0.04 s GPU @ 2.5 Ghz (Python)
371 LWLANet code 22.84 % 34.73 % 19.52 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
372 MonoCD code 22.81 % 33.41 % 19.57 % n/a s 1 core @ 2.5 Ghz (Python)
L. Yan, P. Yan, S. Xiong, X. Xiang and Y. Tan: MonoCD: Monocular 3D Object Detection with Complementary Depths. CVPR 2024.
373 MonoFRD 22.77 % 29.65 % 20.41 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
374 DID-M3D code 22.76 % 32.95 % 19.83 % 0.04 s 1 core @ 2.5 Ghz (Python)
L. Peng, X. Wu, Z. Yang, H. Liu and D. Cai: DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection. ECCV 2022.
375 OPA-3D code 22.53 % 33.54 % 19.22 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
376 MonoSTL 22.42 % 32.19 % 19.48 % na s 1 core @ 2.5 Ghz (Python)
377 MonoCDiT 21.97 % 30.32 % 18.80 % 0.05 s GPU @ >3.5 Ghz (Python)
378 MonoRoIDepth 21.85 % 32.11 % 18.20 % 1 s 1 core @ 2.5 Ghz (C/C++)
379 BA2-Det+MonoFlex 21.54 % 31.61 % 18.35 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
380 DCD code 21.50 % 32.55 % 18.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Y. Li, Y. Chen, J. He and Z. Zhang: Densely Constrained Depth Estimator for Monocular 3D Object Detection. European Conference on Computer Vision 2022.
381 MonoDETR code 21.45 % 32.20 % 18.68 % 0.04 s 1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.
382 SGM3D code 21.37 % 31.49 % 18.43 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
383 Cube R-CNN code 21.20 % 31.70 % 18.43 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
384 GUPNet code 21.19 % 30.29 % 18.20 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
385 MonoSIM_v2 21.19 % 30.36 % 18.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
386 DE_Fusion 21.14 % 33.32 % 18.37 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
387 HomoLoss(monoflex) code 20.68 % 29.60 % 17.81 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
388 DEVIANT code 20.44 % 29.65 % 17.43 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
389 MonoDTR 20.38 % 28.59 % 17.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
390 MDSNet 20.14 % 32.81 % 15.77 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
391 MonoSIM 20.09 % 28.68 % 18.28 % 0.16 s 1 core @ 2.5 Ghz (Python)
392 AutoShape code 20.08 % 30.66 % 15.95 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
393 MonoFlex 19.75 % 28.23 % 16.89 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
394 MonoEF 19.70 % 29.03 % 17.26 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
395 MonOAPC 19.67 % 28.91 % 16.99 % 0035 s 1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
396 HomoLoss(imvoxelnet) code 19.25 % 29.18 % 16.21 % 0.20 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
397 DFR-Net 19.17 % 28.17 % 14.84 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
398 DLE code 19.05 % 31.09 % 14.13 % 0.06 s NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
399 PCT code 19.03 % 29.65 % 15.92 % 0.045 s 1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
400 CaDDN code 18.91 % 27.94 % 17.19 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
401 monodle code 18.89 % 24.79 % 16.00 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
402 Neighbor-Vote 18.65 % 27.39 % 16.54 % 0.1 s GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
403 MonoRCNN++ code 18.62 % 27.20 % 15.69 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
404 GrooMeD-NMS code 18.27 % 26.19 % 14.05 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
405 MonoRCNN code 18.11 % 25.48 % 14.10 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
406 Ground-Aware code 17.98 % 29.81 % 13.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
407 Aug3D-RPN 17.89 % 26.00 % 14.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
408 DDMP-3D 17.89 % 28.08 % 13.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
409 IAFA 17.88 % 25.88 % 15.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
410 FMF-occlusion-net 17.60 % 27.39 % 13.25 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
411 RefinedMPL 17.60 % 28.08 % 13.95 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
412 Kinematic3D code 17.52 % 26.69 % 13.10 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
413 MonoAuxNorm 17.38 % 23.43 % 14.74 % 0.02 s GPU @ 2.5 Ghz (Python)
414 MonoRUn code 17.34 % 27.94 % 15.24 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
415 AM3D 17.32 % 25.03 % 14.91 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
416 YoloMono3D code 17.15 % 26.79 % 12.56 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
417 CMAN 17.04 % 25.89 % 12.88 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
418 GAC3D 16.93 % 25.80 % 12.50 % 0.25 s 1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
419 PatchNet code 16.86 % 22.97 % 14.97 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
420 PGD-FCOS3D code 16.51 % 26.89 % 13.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
421 ImVoxelNet code 16.37 % 25.19 % 13.58 % 0.2 s GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
422 KM3D code 16.20 % 23.44 % 14.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
423 D4LCN code 16.02 % 22.51 % 12.55 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
424 MonoAIU 15.68 % 23.01 % 12.87 % 0.03 s GPU @ 2.5 Ghz (Python)
425 MonoPair 14.83 % 19.28 % 12.89 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
426 Decoupled-3D 14.82 % 23.16 % 11.25 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
427 QD-3DT
This is an online method (no batch processing).
code 14.71 % 20.16 % 12.76 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
428 SMOKE code 14.49 % 20.83 % 12.75 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
429 RTM3D code 14.20 % 19.17 % 11.99 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
430 Mono3D_PLiDAR code 13.92 % 21.27 % 11.25 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
431 M3D-RPN code 13.67 % 21.02 % 10.23 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
432 CSoR
This method makes use of Velodyne laser scans.
13.07 % 18.67 % 10.34 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
433 mdab 12.67 % 18.79 % 10.41 % 0.02 s 1 core @ 2.5 Ghz (Python)
434 MonoPSR code 12.58 % 18.33 % 9.91 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
435 Plane-Constraints code 12.06 % 17.31 % 10.05 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
436 MonoCInIS 11.64 % 22.28 % 9.95 % 0,13 s GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
437 SS3D 11.52 % 16.33 % 9.93 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
438 mdab 11.47 % 17.81 % 9.08 % 0.02 s 1 core @ 2.5 Ghz (Python)
439 MonoGRNet code 11.17 % 18.19 % 8.73 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
440 MonoFENet 11.03 % 17.03 % 9.05 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
441 MonoCInIS 10.96 % 20.42 % 9.23 % 0,14 s GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
442 A3DODWTDA (image) code 8.66 % 10.37 % 7.06 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
443 TLNet (Stereo)
This method uses stereo information.
code 7.69 % 13.71 % 6.73 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
444 Shift R-CNN (mono) code 6.82 % 11.84 % 5.27 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
445 SparVox3D 6.39 % 10.20 % 5.06 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
446 GS3D 6.08 % 8.41 % 4.94 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
447 MVRA + I-FRCNN+ 5.84 % 9.05 % 4.50 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
448 WeakM3D code 5.66 % 11.82 % 4.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
449 ROI-10D 4.91 % 9.78 % 3.74 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
450 3D-GCK 4.57 % 5.79 % 3.64 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
451 FQNet 3.23 % 5.40 % 2.46 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
452 3D-SSMFCNN code 2.63 % 3.20 % 2.40 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
453 VeloFCN
This method makes use of Velodyne laser scans.
0.14 % 0.02 % 0.21 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
454 multi-task CNN 0.00 % 0.00 % 0.00 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
455 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 PiFeNet code 53.92 % 63.25 % 50.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.
2 CasA++ code 53.84 % 60.14 % 51.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
3 TED code 53.48 % 60.13 % 50.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
4 IMLIDAR(base) 53.36 % 62.73 % 49.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 UPIDet code 53.32 % 58.91 % 50.82 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
6 EQ-PVRCNN code 52.81 % 61.73 % 49.87 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
7 VPFNet code 52.41 % 60.07 % 50.28 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
8 Frustum-PointPillars code 52.23 % 60.98 % 48.30 % 0.06 s 4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
9 LGSLNet 52.09 % 60.15 % 48.71 % 0.1 s GPU @ 2.5 Ghz (Python)
10 LoGoNet code 52.06 % 58.24 % 49.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
11 TANet code 51.38 % 60.85 % 47.54 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
12 focalnet 51.38 % 58.82 % 49.23 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
13 CasA code 51.37 % 57.95 % 49.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
14 focalnet 51.34 % 58.94 % 49.21 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
15 SDGUFusion 51.00 % 58.58 % 48.72 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
16 MLF-DET 50.88 % 56.45 % 47.60 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
17 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 50.57 % 59.86 % 46.74 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
18 DPPFA-Net 50.55 % 57.02 % 47.25 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Wang, X. Kong, H. Nishikawa, Q. Lian and H. Tomiyama: Dynamic Point-Pixel Feature Alignment for Multi-modal 3D Object Detection. IEEE Internet of Things Journal 2023.
19 HotSpotNet 50.53 % 57.39 % 46.65 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
20 OGMMDet code 50.50 % 57.39 % 46.76 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
21 ANM code 50.50 % 57.39 % 46.76 % ANM ANM
22 VMVS
This method makes use of Velodyne laser scans.
50.34 % 60.34 % 46.45 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
23 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.32 % 58.49 % 46.98 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
24 3DSSD code 49.94 % 60.54 % 45.73 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
25 PointPainting
This method makes use of Velodyne laser scans.
49.93 % 58.70 % 46.29 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
26 SemanticVoxels 49.93 % 58.91 % 47.31 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
27 ACDet code 49.82 % 58.35 % 47.17 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
28 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 49.81 % 59.04 % 45.92 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
29 USVLab BSAODet code 49.75 % 56.05 % 47.59 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
30 ACFNet 49.74 % 58.07 % 47.27 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
31 F-PointNet
This method makes use of Velodyne laser scans.
code 49.57 % 57.13 % 45.48 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
32 IOUFusion 49.45 % 57.53 % 45.42 % 0.1 s GPU @ 2.5 Ghz (Python)
33 af 49.12 % 55.95 % 46.90 % 1 s GPU @ 2.5 Ghz (Python)
34 F-ConvNet
This method makes use of Velodyne laser scans.
code 48.96 % 57.04 % 44.33 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
35 HVNet 48.86 % 54.84 % 46.33 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
36 CAT-Det 48.78 % 57.13 % 45.56 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
37 STD code 48.72 % 60.02 % 44.55 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
38 PSMS-Net
This method makes use of Velodyne laser scans.
48.66 % 55.15 % 45.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
39 PointPillars
This method makes use of Velodyne laser scans.
code 48.64 % 57.60 % 45.78 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
40 focal 48.56 % 55.56 % 46.42 % 100 s 1 core @ 2.5 Ghz (Python)
41 EPNet++ 48.47 % 56.24 % 45.73 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
42 MGAF-3DSSD code 48.46 % 56.09 % 44.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
43 FIRM-Net 48.37 % 55.63 % 45.97 % 0.07 s 1 core @ 2.5 Ghz (Python)
44 Fast-CLOCs 48.27 % 57.19 % 44.55 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
45 RPF3D 48.24 % 55.50 % 45.80 % 0.1 s 1 core @ 2.5 Ghz (Python)
46 FromVoxelToPoint code 48.15 % 56.54 % 45.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
47 R^2 R-CNN 48.10 % 56.04 % 45.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 SFA-GCL code 47.98 % 56.37 % 44.08 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
49 SFA-GCL_dataaug code 47.95 % 56.33 % 44.07 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
50 EOTL code 47.80 % 56.52 % 43.36 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
51 RAFDet 47.80 % 55.20 % 45.34 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
52 HMFI code 47.77 % 55.61 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
53 casxv1 47.75 % 55.52 % 44.19 % 0.01 s 1 core @ 2.5 Ghz (Python)
54 SFA-GCL(baseline) code 47.69 % 55.95 % 43.91 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
55 IIOU code 47.41 % 54.83 % 43.70 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
56 LVFSD 47.41 % 55.85 % 44.77 % 0.06 s
ERROR: Wrong syntax in BIBTEX file.
57 P2V-RCNN 47.36 % 54.15 % 45.10 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
58 CZY_PPF_Net 47.22 % 51.95 % 45.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 PIPC-3Ddet code 47.22 % 52.71 % 44.02 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
60 Point-GNN
This method makes use of Velodyne laser scans.
code 47.07 % 55.36 % 44.61 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
61 3ONet 47.05 % 56.76 % 44.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
62 OFFNet 47.05 % 53.85 % 44.75 % 0.1 s GPU @ 2.5 Ghz (Python)
63 LGNet-3classes code 46.89 % 52.46 % 44.77 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
64 BPG3D 46.83 % 52.80 % 44.70 % 0.05 s 1 core @ 2.5 Ghz (Python)
65 KPTr 46.83 % 53.98 % 44.56 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
66 F3D 46.77 % 53.69 % 43.89 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
67 SCNet
This method makes use of Velodyne laser scans.
46.73 % 56.87 % 42.74 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
68 PA-Det3D 46.55 % 53.88 % 44.13 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
69 DGEnhCL code 46.53 % 56.39 % 42.65 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
70 casx 46.44 % 54.61 % 42.83 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
71 PASS-PV-RCNN-Plus 46.36 % 51.47 % 44.10 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
72 RAFDet code 46.32 % 53.65 % 42.98 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
73 VPA 46.23 % 52.37 % 42.84 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
74 SFA-GCL(80, k=4) code 46.16 % 55.88 % 43.84 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
75 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 46.13 % 54.77 % 42.84 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
76 DA-Net 45.96 % 56.48 % 41.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 ARPNET 45.92 % 55.48 % 42.54 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
78 SFA-GCL(80) code 45.85 % 55.34 % 42.02 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
79 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 45.82 % 52.03 % 43.81 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
80 DFSemONet(Baseline) 45.68 % 55.82 % 42.13 % 0.04 s GPU @ 2.5 Ghz (Python)
81 SVGA-Net 45.68 % 53.09 % 43.30 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
82 epBRM
This method makes use of Velodyne laser scans.
code 45.49 % 52.48 % 42.75 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
83 PG-RCNN code 45.48 % 51.63 % 43.30 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
84 MG 45.46 % 51.71 % 42.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 PDV code 45.45 % 51.95 % 43.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
86 MLOD
This method makes use of Velodyne laser scans.
code 45.40 % 55.09 % 41.42 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
87 HA-PillarNet 45.26 % 50.50 % 43.21 % 0.05 s 1 core @ 2.5 Ghz (Python)
88 centerpoint_pcdet 45.22 % 51.41 % 43.05 % 0.06 s 1 core @ 2.5 Ghz (Python)
89 IA-SSD (single) code 45.07 % 52.73 % 42.75 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
90 DiffCandiDet 45.02 % 52.45 % 41.24 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
91 DFAF3D 45.01 % 52.86 % 42.73 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
92 voxelnext_pcdet 44.91 % 52.43 % 42.56 % 0.05 s 1 core @ 2.5 Ghz (Python)
93 SRDL 44.84 % 52.42 % 42.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
94 M3DeTR code 44.78 % 50.63 % 42.57 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
95 U_PV_V2_ep100_80 44.67 % 49.71 % 42.64 % 0... s 1 core @ 2.5 Ghz (Python)
96 Anonymous code 44.66 % 53.73 % 42.58 % 0.04 s 1 core @ 2.5 Ghz (Python)
97 PI-SECOND code 44.64 % 54.33 % 40.68 % 0.05 s GPU @ >3.5 Ghz (Python + C/C++)
98 PV-RCNN-Plus 44.61 % 51.20 % 42.55 % 1 s 1 core @ 2.5 Ghz (C/C++)
99 TF-PartA2 44.47 % 53.22 % 41.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
100 U_second_v4_ep_100_8 44.46 % 51.36 % 42.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
101 u_second_v4_epoch_10 44.32 % 50.97 % 42.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
102 SIF 44.28 % 52.05 % 42.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
103 BAPartA2S-4h 44.18 % 53.05 % 41.54 % 0.1 s 1 core @ 2.5 Ghz (Python)
104 bs 44.18 % 50.59 % 41.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
105 U_PV_V2_ep_100_100 44.18 % 49.62 % 42.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
106 CG-SSD 44.17 % 50.84 % 42.02 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
107 SFA-GCL code 44.17 % 53.53 % 41.97 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
108 DVFENet 44.12 % 50.98 % 41.62 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
109 Test_dif code 43.97 % 51.07 % 41.05 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
110 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 43.85 % 52.15 % 41.68 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
111 RAFDet 43.81 % 51.37 % 41.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
112 HAF-PVP_test 43.70 % 50.24 % 40.12 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
113 SC-SSD 43.64 % 50.05 % 41.56 % 1 s 1 core @ 2.5 Ghz (C/C++)
114 DDF 43.64 % 52.64 % 41.42 % 0.1 s 1 core @ 2.5 Ghz (Python)
115 PR-SSD 43.58 % 50.38 % 41.36 % 0.02 s GPU @ 2.5 Ghz (Python)
116 GF-pointnet 43.47 % 50.12 % 41.30 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
117 GeVo 43.46 % 47.87 % 41.53 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
118 S-AT GCN 43.43 % 50.63 % 41.58 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
119 AAMVFNet code 43.29 % 49.38 % 40.33 % 0.04 s GPU @ 2.5 Ghz (Python)
120 BirdNet+
This method makes use of Velodyne laser scans.
code 42.87 % 48.90 % 40.59 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
121 L-AUG 42.84 % 50.32 % 40.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
122 XT-PartA2 42.68 % 50.62 % 40.25 % 0.1 s GPU @ >3.5 Ghz (Python)
123 IA-SSD (multi) code 42.61 % 51.76 % 40.51 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
124 AMVFNet code 42.61 % 50.04 % 39.35 % 0.04 s GPU @ 2.5 Ghz (Python)
125 prcnn_v18_80_100 42.48 % 50.92 % 38.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
126 XView 42.42 % 47.24 % 39.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
127 PVTr 42.14 % 48.76 % 40.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
128 GraphAlign(ICCV2023) code 41.95 % 46.61 % 40.05 % 0.03 s GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
129 SeSame-voxel code 41.59 % 50.12 % 37.79 % N/A s TITAN RTX @ 1.35 Ghz (Python)
130 HINTED 41.55 % 53.09 % 39.18 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
131 PUDet 41.48 % 50.24 % 39.22 % 0.3 s GPU @ 2.5 Ghz (Python)
132 MVAF-Net(3-classes) 41.47 % 50.56 % 38.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
133 mm3d_PartA2 41.24 % 48.45 % 38.92 % 0.1 s GPU @ >3.5 Ghz (Python)
134 SeSame-point code 41.22 % 48.25 % 39.18 % N/A s TITAN RTX @ 1.35 Ghz (Python)
135 PFF3D
This method makes use of Velodyne laser scans.
code 40.94 % 48.74 % 38.54 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
136 HA PillarNet 40.73 % 49.38 % 38.11 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
137 MMpointpillars 40.43 % 47.85 % 37.68 % 0.05 s 1 core @ 2.5 Ghz (Python)
138 VoxelFSD-S 40.39 % 47.66 % 38.12 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
139 VSAC 40.37 % 49.91 % 36.64 % 0.07 s 1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
140 IIOU_LDR code 40.24 % 48.51 % 37.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
141 MM_SECOND code 40.22 % 49.46 % 37.46 % 0.05 s GPU @ >3.5 Ghz (Python)
142 ROT_S3D 40.08 % 46.62 % 38.33 % 0.1 s GPU @ 2.5 Ghz (Python)
143 DSGN++
This method uses stereo information.
code 38.92 % 50.26 % 35.12 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
144 MVAF-Net(3-classes) 38.87 % 46.92 % 36.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
145 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 38.79 % 47.51 % 35.85 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
146 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 38.28 % 45.53 % 35.37 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
147 MMpp 38.07 % 45.74 % 35.75 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
148 CSW3D
This method makes use of Velodyne laser scans.
37.96 % 49.27 % 33.83 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
149 StereoDistill 37.75 % 50.79 % 34.28 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
150 SeSame-pillar code 37.31 % 44.21 % 35.17 % N/A s TITAN RTX @ 1.35 Ghz (Python)
151 P2P code 36.71 % 44.67 % 34.86 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
152 DMF
This method uses stereo information.
34.92 % 42.08 % 32.69 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
153 SparsePool code 34.15 % 43.33 % 31.78 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
154 MMLAB LIGA-Stereo
This method uses stereo information.
code 34.13 % 44.71 % 30.42 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
155 SeSame-voxel w/score code 33.76 % 39.42 % 31.31 % N/A s GPU @ 1.5 Ghz (Python)
156 AVOD
This method makes use of Velodyne laser scans.
code 33.57 % 42.58 % 30.14 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
157 SparsePool code 33.22 % 41.55 % 29.66 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
158 fuf 33.16 % 41.95 % 29.75 % 10 s 1 core @ 2.5 Ghz (C/C++)
159 SeSame-pillar w/scor code 32.78 % 39.11 % 30.87 % N/A s 1 core @ 2.5 Ghz (C/C++)
160 ODGS 31.23 % 37.54 % 28.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
161 SFEBEV 31.04 % 38.14 % 29.09 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
162 CG-Stereo
This method uses stereo information.
29.56 % 39.24 % 25.87 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
163 PointRGBNet 29.32 % 38.07 % 26.94 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
164 Disp R-CNN
This method uses stereo information.
code 29.12 % 42.72 % 25.09 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
165 Disp R-CNN (velo)
This method uses stereo information.
code 28.34 % 40.21 % 24.46 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
166 SeSame-point w/score code 25.79 % 33.98 % 22.50 % N/A s GPU @ 1.5 Ghz (Python)
167 BirdNet
This method makes use of Velodyne laser scans.
23.06 % 28.20 % 21.65 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
168 TS3D
This method uses stereo information.
21.69 % 32.90 % 19.27 % 0.09 s GPU @ 1.5 Ghz (Python + C/C++)
169 OC Stereo
This method uses stereo information.
code 20.80 % 29.79 % 18.62 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
170 YOLOStereo3D
This method uses stereo information.
code 20.76 % 31.01 % 18.41 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
171 DSGN
This method uses stereo information.
code 20.75 % 26.61 % 18.86 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
172 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.26 % 21.42 % 17.06 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
173 BKDStereo3D code 17.44 % 25.47 % 14.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
174 BKDStereo3D w/o KD code 16.87 % 23.82 % 14.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
175 TopNet-Retina
This method makes use of Velodyne laser scans.
14.57 % 18.04 % 12.48 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
176 RT3D-GMP
This method uses stereo information.
14.22 % 19.92 % 12.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
177 MonoLTKD_V3 13.62 % 19.79 % 11.92 % 0.04 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
178 TopNet-HighRes
This method makes use of Velodyne laser scans.
13.50 % 19.43 % 11.93 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
179 MonoTAKD V2 13.47 % 19.67 % 11.75 % 0.1 s 1 core @ 2.5 Ghz (Python)
180 ESGN
This method uses stereo information.
13.03 % 17.94 % 11.54 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
181 SST [st]
This method uses stereo information.
12.66 % 19.18 % 11.07 % 1 s 1 core @ 2.5 Ghz (Python)
182 DD3D code 12.51 % 18.58 % 10.65 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
183 MonoLSS 12.34 % 18.40 % 10.54 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
184 PS-fld code 12.23 % 19.03 % 10.53 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
185 MonoTAKD 12.15 % 18.23 % 10.50 % 0.1 s 1 core @ 2.5 Ghz (Python)
186 CIE 11.94 % 17.90 % 10.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
187 MonoLTKD 11.85 % 17.74 % 10.26 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
188 SVDM-VIEW 11.11 % 16.66 % 9.46 % 1 s 1 core @ 2.5 Ghz (Python)
189 OPA-3D code 11.01 % 17.14 % 9.94 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
190 MonoUNI code 10.90 % 16.54 % 9.17 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
191 MonoDTR 10.59 % 16.66 % 9.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
192 MonoFRD 10.38 % 15.68 % 8.79 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
193 GUPNet code 10.37 % 15.62 % 8.79 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
194 CMKD code 10.28 % 16.03 % 8.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
195 MonoSIM 10.08 % 15.80 % 8.64 % 0.16 s 1 core @ 2.5 Ghz (Python)
196 MonoInsight 9.98 % 15.20 % 8.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
197 MonoInsight 9.98 % 15.20 % 8.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
198 DEVIANT code 9.77 % 14.49 % 8.28 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
199 MonoNeRD code 9.66 % 15.27 % 8.28 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
200 CaDDN code 9.41 % 14.72 % 8.17 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
201 SGM3D code 9.39 % 15.39 % 8.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
202 MonoRCNN++ code 9.04 % 13.45 % 7.74 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
203 HomoLoss(monoflex) code 8.81 % 13.26 % 7.41 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
204 MonoSIM_v2 8.62 % 13.13 % 7.27 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
205 MonoDDE 8.41 % 12.38 % 7.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
206 Mix-Teaching code 8.40 % 12.34 % 7.06 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
207 MDSNet 8.18 % 12.05 % 7.03 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
208 LPCG-Monoflex code 7.92 % 12.11 % 6.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
209 RefinedMPL 7.92 % 13.09 % 7.25 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
210 Cube R-CNN code 7.65 % 11.67 % 6.60 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
211 MonoRUn code 7.59 % 11.70 % 6.34 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
212 MonoFlex 7.36 % 10.36 % 6.29 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
213 MonoTRKDv2 7.22 % 11.05 % 6.11 % 40 s 1 core @ 2.5 Ghz (Python)
214 DA3D+KM3D+v2-99 7.06 % 10.32 % 6.10 % 0.120s GPU @ 2.5 Ghz (Python)
215 MonoPair 7.04 % 10.99 % 6.29 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
216 monodle code 6.96 % 10.73 % 6.20 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
217 MonOAPC 6.82 % 9.62 % 5.78 % 0035 s 1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
218 SH3D 6.75 % 10.26 % 5.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
219 TopNet-DecayRate
This method makes use of Velodyne laser scans.
6.59 % 8.78 % 6.25 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
220 MonoAIU 6.43 % 9.55 % 5.39 % 0.03 s GPU @ 2.5 Ghz (Python)
221 mdab 6.36 % 10.26 % 5.62 % 0.02 s 1 core @ 2.5 Ghz (Python)
222 Anonymous 6.25 % 9.22 % 5.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
223 Shift R-CNN (mono) code 5.66 % 8.58 % 4.49 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
224 FMF-occlusion-net 5.62 % 8.69 % 5.25 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
225 Aug3D-RPN 5.22 % 7.14 % 4.21 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
226 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.60 % 6.88 % 3.79 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
227 MonoPSR code 4.56 % 7.24 % 4.11 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
228 DFR-Net 4.52 % 6.66 % 3.71 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
229 MonoGhost_Ped_Cycl 4.49 % 7.14 % 4.39 % 0.03 s 1 core @ 2.5 Ghz (Python)
230 QD-3DT
This is an online method (no batch processing).
code 4.23 % 6.62 % 3.39 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
231 DA3D+KM3D 4.05 % 5.94 % 3.55 % 0.02 s GPU @ 2.5 Ghz (Python)
232 M3D-RPN code 4.05 % 5.65 % 3.29 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
233 DDMP-3D 4.02 % 5.53 % 3.36 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
234 CMAN 3.96 % 5.24 % 3.18 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
235 D4LCN code 3.86 % 5.06 % 3.59 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
236 RT3DStereo
This method uses stereo information.
3.65 % 4.72 % 3.00 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
237 DA3D 3.27 % 4.93 % 2.74 % 0.03 s 1 core @ 2.5 Ghz (Python)
238 MonoEF 3.05 % 4.61 % 2.85 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
239 MonoLiG code 2.72 % 3.74 % 2.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
240 mdab 2.43 % 3.68 % 2.26 % 0.02 s 1 core @ 2.5 Ghz (Python)
241 SS3D 2.09 % 2.48 % 1.61 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
242 SparVox3D 2.05 % 2.90 % 1.69 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
243 PGD-FCOS3D code 1.88 % 2.82 % 1.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
244 MonoAuxNorm 1.24 % 1.58 % 0.96 % 0.02 s GPU @ 2.5 Ghz (Python)
245 Plane-Constraints code 1.16 % 1.87 % 1.13 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
246 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 UPIDet code 78.19 % 89.65 % 71.13 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
2 CasA++ code 76.99 % 88.93 % 70.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
3 TED code 76.95 % 89.54 % 70.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
4 CasA code 75.74 % 88.99 % 68.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
5 LoGoNet code 74.92 % 85.85 % 67.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
6 MLF-DET 74.88 % 86.20 % 66.75 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
7 USVLab BSAODet code 74.38 % 85.01 % 67.38 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
8 PSMS-Net
This method makes use of Velodyne laser scans.
74.30 % 85.06 % 66.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
9 IMLIDAR(base) 74.23 % 87.39 % 66.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 HMFI code 74.06 % 85.69 % 67.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
11 VPA 73.91 % 84.94 % 66.92 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
12 CZY_PPF_Net 73.64 % 85.39 % 66.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
13 EQ-PVRCNN code 73.30 % 86.25 % 65.49 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
14 U_PV_V2_ep100_80 73.17 % 86.95 % 66.01 % 0... s 1 core @ 2.5 Ghz (Python)
15 OGMMDet code 72.92 % 86.07 % 65.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
16 ANM code 72.92 % 86.07 % 65.95 % ANM ANM
17 OFFNet 72.74 % 83.33 % 67.53 % 0.1 s GPU @ 2.5 Ghz (Python)
18 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 72.61 % 83.93 % 65.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
19 CAT-Det 72.51 % 85.35 % 65.55 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
20 HA-PillarNet 72.50 % 86.25 % 65.38 % 0.05 s 1 core @ 2.5 Ghz (Python)
21 KPTr 72.24 % 83.83 % 63.94 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
22 PA-Det3D 71.93 % 84.41 % 65.36 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
23 GF-pointnet 71.90 % 84.28 % 63.75 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
24 BtcDet
This method makes use of Velodyne laser scans.
code 71.76 % 84.48 % 64.70 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
25 ACFNet 71.68 % 85.76 % 65.33 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
26 Anonymous code 71.61 % 86.04 % 63.31 % 0.04 s 1 core @ 2.5 Ghz (Python)
27 focalnet 71.57 % 82.10 % 65.37 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
28 PointPainting
This method makes use of Velodyne laser scans.
71.54 % 83.91 % 62.97 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
29 PIPC-3Ddet code 71.53 % 85.58 % 64.66 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
30 PASS-PV-RCNN-Plus 71.51 % 83.03 % 63.85 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
31 PV-RCNN-Plus 71.51 % 83.83 % 64.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
32 RangeIoUDet
This method makes use of Velodyne laser scans.
71.49 % 85.99 % 63.62 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
33 ACDet code 71.48 % 87.76 % 64.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
34 IA-SSD (single) code 71.44 % 85.91 % 63.41 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
35 U_PV_V2_ep_100_100 71.35 % 84.08 % 63.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
36 PDV code 71.31 % 85.54 % 64.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
37 3ONet 71.29 % 85.17 % 62.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
38 DFAF3D 71.27 % 85.75 % 64.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
39 BPG3D 71.24 % 85.28 % 63.42 % 0.05 s 1 core @ 2.5 Ghz (Python)
40 focalnet 71.24 % 81.78 % 65.37 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
41 HVNet 71.17 % 83.97 % 63.65 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
42 DiffCandiDet 71.11 % 85.33 % 64.52 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
43 RAFDet 70.99 % 84.92 % 62.93 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
44 M3DeTR code 70.89 % 85.03 % 63.14 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
45 PR-SSD 70.88 % 83.44 % 63.43 % 0.02 s GPU @ 2.5 Ghz (Python)
46 HAF-PVP_test 70.66 % 83.99 % 62.42 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
47 PG-RCNN code 70.65 % 84.94 % 64.03 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
48 AAMVFNet code 70.52 % 84.47 % 63.85 % 0.04 s GPU @ 2.5 Ghz (Python)
49 LGNet-3classes code 70.44 % 81.32 % 62.95 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
50 AMVFNet code 70.44 % 83.98 % 63.87 % 0.04 s GPU @ 2.5 Ghz (Python)
51 DDF 70.23 % 86.04 % 63.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
52 SPG_mini
This method makes use of Velodyne laser scans.
code 70.09 % 82.66 % 63.61 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
53 SDGUFusion 70.05 % 81.15 % 63.98 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
54 RAFDet code 69.81 % 82.41 % 62.17 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
55 RAFDet 69.79 % 81.93 % 62.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
56 PVTr 69.56 % 84.79 % 62.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 GeVo 69.56 % 83.03 % 62.74 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
58 GraphAlign(ICCV2023) code 69.43 % 80.71 % 63.57 % 0.03 s GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
59 F3D 69.13 % 84.32 % 62.24 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
60 LGSLNet 69.11 % 81.67 % 64.15 % 0.1 s GPU @ 2.5 Ghz (Python)
61 u_second_v4_epoch_10 69.10 % 84.24 % 62.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 FIRM-Net 69.09 % 82.99 % 62.48 % 0.07 s 1 core @ 2.5 Ghz (Python)
63 DA-Net 69.04 % 84.10 % 64.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 68.89 % 82.49 % 62.41 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
65 F-ConvNet
This method makes use of Velodyne laser scans.
code 68.88 % 84.16 % 60.05 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
66 IIOU code 68.82 % 83.89 % 60.14 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
67 RPF3D 68.81 % 82.97 % 62.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
68 bs 68.73 % 82.32 % 62.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
69 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 68.73 % 83.43 % 61.85 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
70 casx 68.69 % 82.89 % 60.75 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
71 U_second_v4_ep_100_8 68.62 % 82.37 % 61.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 HotSpotNet 68.51 % 83.29 % 61.84 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
73 CG-SSD 68.24 % 79.80 % 61.05 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
74 P2V-RCNN 68.06 % 81.09 % 60.73 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
75 SFA-GCL(80) code 68.06 % 84.65 % 61.18 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
76 H^23D R-CNN code 67.90 % 82.76 % 60.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
77 SFA-GCL code 67.72 % 84.16 % 60.89 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
78 focal 67.67 % 80.82 % 61.88 % 100 s 1 core @ 2.5 Ghz (Python)
79 VPFNet code 67.66 % 80.83 % 61.36 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
80 3DSSD code 67.62 % 85.04 % 61.14 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
81 Fast-CLOCs 67.55 % 83.34 % 59.61 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
82 SFA-GCL(80, k=4) code 67.46 % 84.31 % 58.87 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
83 XT-PartA2 67.40 % 81.41 % 61.92 % 0.1 s GPU @ >3.5 Ghz (Python)
84 DVFENet 67.40 % 82.29 % 60.71 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
85 FromVoxelToPoint code 67.36 % 82.68 % 59.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
86 Point-GNN
This method makes use of Velodyne laser scans.
code 67.28 % 81.17 % 59.67 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
87 HINTED 67.27 % 81.53 % 60.88 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
88 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 67.24 % 82.56 % 60.28 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
89 STD code 67.23 % 81.36 % 59.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
90 mm3d_PartA2 67.12 % 82.10 % 60.42 % 0.1 s GPU @ >3.5 Ghz (Python)
91 BAPartA2S-4h 67.05 % 82.22 % 61.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
92 SVGA-Net 66.82 % 81.25 % 59.37 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
93 S-AT GCN 66.71 % 78.53 % 60.19 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
94 TF-PartA2 66.67 % 82.42 % 60.64 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
95 casxv1 66.50 % 81.01 % 60.09 % 0.01 s 1 core @ 2.5 Ghz (Python)
96 ARPNET 66.39 % 82.32 % 58.80 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
97 IA-SSD (multi) code 66.29 % 81.30 % 59.58 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
98 MGAF-3DSSD code 66.00 % 83.03 % 57.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
99 IOUFusion 65.88 % 82.98 % 59.11 % 0.1 s GPU @ 2.5 Ghz (Python)
100 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 65.85 % 80.00 % 58.69 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
101 EOTL code 65.76 % 81.44 % 56.47 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
102 PI-SECOND code 65.62 % 81.99 % 59.19 % 0.05 s GPU @ >3.5 Ghz (Python + C/C++)
103 MG 65.43 % 81.05 % 59.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
104 SC-SSD 65.36 % 79.14 % 58.50 % 1 s 1 core @ 2.5 Ghz (C/C++)
105 SFA-GCL code 65.22 % 82.10 % 56.54 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
106 af 65.12 % 78.85 % 59.17 % 1 s GPU @ 2.5 Ghz (Python)
107 DGEnhCL code 65.07 % 81.38 % 58.13 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
108 centerpoint_pcdet 64.99 % 79.83 % 58.43 % 0.06 s 1 core @ 2.5 Ghz (Python)
109 Test_dif code 64.80 % 80.24 % 58.49 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
110 voxelnext_pcdet 64.66 % 81.10 % 57.53 % 0.05 s 1 core @ 2.5 Ghz (Python)
111 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 64.54 % 79.65 % 57.84 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
112 SRDL 64.52 % 79.64 % 57.90 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
113 VoxelFSD-S 64.26 % 80.07 % 57.17 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
114 SIF 64.13 % 79.32 % 57.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
115 fuf 64.12 % 82.07 % 56.34 % 10 s 1 core @ 2.5 Ghz (C/C++)
116 prcnn_v18_80_100 63.87 % 80.66 % 57.25 % 0.1 s 1 core @ 2.5 Ghz (Python)
117 TANet code 63.77 % 79.16 % 56.21 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
118 SFA-GCL_dataaug code 63.35 % 81.93 % 56.47 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
119 ROT_S3D 63.26 % 79.60 % 56.95 % 0.1 s GPU @ 2.5 Ghz (Python)
120 SFA-GCL(baseline) code 63.24 % 81.50 % 56.42 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
121 XView 63.06 % 81.32 % 56.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
122 EPNet++ 62.94 % 78.57 % 56.62 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
123 PointPillars
This method makes use of Velodyne laser scans.
code 62.73 % 79.90 % 55.58 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
124 MM_SECOND code 62.61 % 77.98 % 55.67 % 0.05 s GPU @ >3.5 Ghz (Python)
125 L-AUG 62.56 % 75.41 % 56.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
126 MMpp 61.70 % 75.76 % 54.75 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
127 IIOU_LDR code 61.70 % 77.26 % 55.43 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
128 SeSame-point code 61.70 % 75.73 % 55.27 % N/A s TITAN RTX @ 1.35 Ghz (Python)
129 LVFSD 61.68 % 79.03 % 55.02 % 0.06 s
ERROR: Wrong syntax in BIBTEX file.
130 F-PointNet
This method makes use of Velodyne laser scans.
code 61.37 % 77.26 % 53.78 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
131 MMpointpillars 61.06 % 74.55 % 55.02 % 0.05 s 1 core @ 2.5 Ghz (Python)
132 P2P code 61.03 % 75.03 % 55.05 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
133 VSAC 60.23 % 78.55 % 53.91 % 0.07 s 1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
134 SeSame-pillar code 60.21 % 72.22 % 53.67 % N/A s TITAN RTX @ 1.35 Ghz (Python)
135 epBRM
This method makes use of Velodyne laser scans.
code 59.79 % 75.13 % 53.36 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
136 BirdNet+
This method makes use of Velodyne laser scans.
code 59.58 % 70.84 % 54.20 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
137 SeSame-voxel code 59.36 % 76.95 % 53.14 % N/A s TITAN RTX @ 1.35 Ghz (Python)
138 SFEBEV 58.28 % 73.10 % 52.31 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
139 DMF
This method uses stereo information.
57.99 % 71.92 % 51.55 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
140 PUDet 57.77 % 72.93 % 51.03 % 0.3 s GPU @ 2.5 Ghz (Python)
141 PointRGBNet 57.59 % 73.09 % 51.78 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
142 HA PillarNet 57.56 % 71.10 % 50.67 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
143 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.12 % 69.39 % 51.09 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
144 PiFeNet code 56.94 % 72.80 % 50.04 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.
145 SCNet
This method makes use of Velodyne laser scans.
56.39 % 73.73 % 49.99 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
146 PFF3D
This method makes use of Velodyne laser scans.
code 55.71 % 72.67 % 49.58 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
147 DFSemONet(Baseline) 55.20 % 73.44 % 49.29 % 0.04 s GPU @ 2.5 Ghz (Python)
148 MLOD
This method makes use of Velodyne laser scans.
code 55.06 % 73.03 % 48.21 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
149 MVAF-Net(3-classes) 52.24 % 68.13 % 47.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
150 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 52.15 % 72.45 % 46.57 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
151 MVAF-Net(3-classes) 51.35 % 66.21 % 46.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
152 DSGN++
This method uses stereo information.
code 49.37 % 68.29 % 43.79 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
153 StereoDistill 48.37 % 69.46 % 42.69 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
154 AVOD
This method makes use of Velodyne laser scans.
code 48.15 % 64.11 % 42.37 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
155 SeSame-voxel w/score code 45.61 % 58.94 % 40.68 % N/A s GPU @ 1.5 Ghz (Python)
156 BirdNet
This method makes use of Velodyne laser scans.
41.56 % 58.64 % 36.94 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
157 SparsePool code 40.74 % 56.52 % 36.68 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
158 MMLAB LIGA-Stereo
This method uses stereo information.
code 40.60 % 58.95 % 35.27 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
159 TopNet-Retina
This method makes use of Velodyne laser scans.
36.83 % 47.48 % 33.58 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
160 CG-Stereo
This method uses stereo information.
36.25 % 55.33 % 32.17 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
161 SparsePool code 35.24 % 43.55 % 30.15 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
162 Disp R-CNN (velo)
This method uses stereo information.
code 27.04 % 44.19 % 23.58 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
163 Disp R-CNN
This method uses stereo information.
code 27.04 % 44.19 % 23.58 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
164 Complexer-YOLO
This method makes use of Velodyne laser scans.
25.43 % 32.00 % 22.88 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
165 DSGN
This method uses stereo information.
code 21.04 % 31.23 % 18.93 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
166 SeSame-pillar w/scor code 19.53 % 15.92 % 17.61 % N/A s 1 core @ 2.5 Ghz (C/C++)
167 OC Stereo
This method uses stereo information.
code 19.23 % 32.47 % 17.11 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
168 TopNet-DecayRate
This method makes use of Velodyne laser scans.
16.00 % 23.02 % 13.24 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
169 SST [st]
This method uses stereo information.
15.20 % 26.40 % 13.47 % 1 s 1 core @ 2.5 Ghz (Python)
170 RT3D-GMP
This method uses stereo information.
13.92 % 20.59 % 12.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
171 MonoTAKD V2 11.66 % 19.68 % 10.33 % 0.1 s 1 core @ 2.5 Ghz (Python)
172 MonoTAKD 11.17 % 17.98 % 9.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
173 MonoLTKD_V3 9.42 % 16.90 % 8.29 % 0.04 s 1 core @ 2.5 Ghz (Python)
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174 TopNet-UncEst
This method makes use of Velodyne laser scans.
9.18 % 12.31 % 8.14 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
175 ESGN
This method uses stereo information.
9.02 % 15.78 % 7.96 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
176 SeSame-point w/score code 8.90 % 10.65 % 7.68 % N/A s GPU @ 1.5 Ghz (Python)
177 MonoLTKD 8.25 % 13.73 % 7.01 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
178 CMKD code 8.15 % 14.66 % 7.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
179 MonoGhost_Ped_Cycl 8.11 % 12.23 % 6.75 % 0.03 s 1 core @ 2.5 Ghz (Python)
180 PS-fld code 7.29 % 12.80 % 6.05 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
181 SVDM-VIEW 6.57 % 11.33 % 5.94 % 1 s 1 core @ 2.5 Ghz (Python)
182 MonoLiG code 6.49 % 9.48 % 5.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
183 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.48 % 9.99 % 6.76 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
184 DA3D+KM3D+v2-99 5.82 % 9.73 % 4.88 % 0.120s GPU @ 2.5 Ghz (Python)
185 MonoPSR code 5.78 % 9.87 % 4.57 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
186 DD3D code 5.69 % 9.20 % 5.20 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
187 MonoSIM_v2 5.61 % 9.09 % 4.77 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
188 MonoLSS 5.52 % 8.88 % 4.98 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
189 CaDDN code 5.38 % 9.67 % 4.75 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
190 Mix-Teaching code 5.36 % 8.56 % 4.62 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
191 Anonymous 5.23 % 8.88 % 4.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
192 MonoUNI code 5.03 % 8.25 % 4.50 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
193 MonoTRKDv2 5.01 % 9.08 % 4.21 % 40 s 1 core @ 2.5 Ghz (Python)
194 MonoInsight 4.97 % 7.40 % 4.09 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
195 MonoInsight 4.97 % 7.40 % 4.09 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
196 LPCG-Monoflex code 4.90 % 8.14 % 3.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
197 Plane-Constraints code 4.79 % 8.67 % 3.90 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
198 MonoFRD 4.55 % 8.44 % 4.14 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
199 MonoDDE 4.36 % 6.68 % 3.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
200 MonoDTR 4.11 % 5.84 % 3.48 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
201 RT3DStereo
This method uses stereo information.
4.10 % 7.03 % 3.88 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
202 HomoLoss(monoflex) code 4.09 % 6.81 % 3.78 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
203 DFR-Net 4.00 % 5.99 % 3.95 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
204 DEVIANT code 3.97 % 6.42 % 3.51 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
205 GUPNet code 3.85 % 6.94 % 3.64 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
206 OPA-3D code 3.75 % 6.01 % 3.56 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
207 CIE 3.74 % 6.13 % 3.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
208 SGM3D code 3.63 % 7.05 % 3.33 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
209 SH3D 3.60 % 6.73 % 3.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
210 mdab 3.38 % 6.94 % 3.21 % 0.02 s 1 core @ 2.5 Ghz (Python)
211 Cube R-CNN code 3.35 % 5.01 % 3.23 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
212 Aug3D-RPN 3.33 % 5.44 % 2.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
213 MonOAPC 3.31 % 6.54 % 3.05 % 0035 s 1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
214 monodle code 3.28 % 5.34 % 2.83 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
215 MDSNet 3.22 % 5.99 % 2.62 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
216 DDMP-3D 3.14 % 4.92 % 2.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
217 MonoSIM 3.05 % 5.40 % 2.60 % 0.16 s 1 core @ 2.5 Ghz (Python)
218 QD-3DT
This is an online method (no batch processing).
code 3.02 % 5.71 % 2.73 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
219 MonoPair 2.87 % 4.76 % 2.42 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
220 MonoNeRD code 2.80 % 5.24 % 2.55 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
221 MonoFlex 2.67 % 4.41 % 2.50 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
222 RefinedMPL 2.42 % 4.23 % 2.14 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
223 MonoRCNN++ code 2.31 % 3.50 % 2.01 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
224 mdab 2.00 % 4.00 % 2.00 % 0.02 s 1 core @ 2.5 Ghz (Python)
225 SS3D 1.89 % 3.45 % 1.44 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
226 DA3D 1.89 % 3.46 % 1.51 % 0.03 s 1 core @ 2.5 Ghz (Python)
227 D4LCN code 1.82 % 2.72 % 1.79 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
228 PGD-FCOS3D code 1.79 % 3.54 % 1.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
229 FMF-occlusion-net 1.65 % 1.91 % 1.75 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
230 MonoAuxNorm 1.65 % 3.00 % 1.37 % 0.02 s GPU @ 2.5 Ghz (Python)
231 CMAN 1.48 % 1.76 % 1.17 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
232 DA3D+KM3D 1.44 % 2.88 % 1.37 % 0.02 s GPU @ 2.5 Ghz (Python)
233 MonoEF 1.18 % 2.36 % 1.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
234 M3D-RPN code 0.81 % 1.25 % 0.78 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
235 MonoRUn code 0.73 % 1.14 % 0.66 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
236 MonoAIU 0.72 % 0.92 % 0.45 % 0.03 s GPU @ 2.5 Ghz (Python)
237 Shift R-CNN (mono) code 0.38 % 0.76 % 0.41 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
238 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}
}



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