Semanic Scene Understanding

3D Bounding Box Detection


We evaluate all methods using mean Average Precision (AP) calculated at a threshold of 0.25 and 0.5, respectively. Our evaluation table ranks all methods according to the AP evaluated at the IoU threshold of 0.5.

Method Setting Code AP 25 AP 50 Runtime Environment
1 PBEV+SeaBird code 37.12 4.64 0.15 s NVIDIA A100
A. Kumar, Y. Guo, X. Huang, L. Ren and X. Liu: SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects. CVPR 2024.
2 BoxNet code 23.59 4.08 NVIDIA V100
C. Qi, O. Litany, K. He and L. Guibas: Deep Hough Voting for 3D Object Detection in Point Clouds. ICCV 2019.
3 VoteNet code 30.61 3.40 NVIDIA V100
C. Qi, O. Litany, K. He and L. Guibas: Deep Hough Voting for 3D Object Detection in Point Clouds. ICCV 2019.
4 I2M+SeaBird code 35.04 3.14 0.02 s NVIDIA A100
A. Kumar, Y. Guo, X. Huang, L. Ren and X. Liu: SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects. CVPR 2024.
5 ANM 25.93 1.60 1 NVIDIA A100
6 DEVIANT code 26.96 0.88 0.04 s NVIDIA A100
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth Equivariant Network for Monocular 3D Object Detection. ECCV 2022.
7 GUP Net code 27.25 0.87 0.02 s NVIDIA A100
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. ICCV 2021.
8 MonoDLE code 28.99 0.85 0.04 s NVIDIA A100
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.
9 Cube R-CNN code 15.57 0.80 0.04 s NVIDIA A100
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.
10 MonoDETR code 27.13 0.79 0.4 s 1 core @ 2.5 Ghz (C/C++)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection. ICCV 2023.
11 GrooMeD-NMS code 16.12 0.17 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.
Table as LaTeX | Only published Methods





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