Papers › BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, Zeming Li
In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View (BEV) 3D object detection. Our work is based on a key observation -- depth estimation in recent approaches is surprisingly inadequate given the fact that depth is essential to camera 3D detection. Our BEVDepth resolves this by leveraging explicit depth supervision. A camera-awareness depth estimation module is also introduced to facilitate the depth predicting capability. Besides, we design a novel Depth Refinement Module to counter the side effects carried by imprecise feature unprojection. Aided by customized Efficient Voxel Pooling and multi-frame mechanism, BEVDepth achieves the new state-of-the-art 60.9% NDS on the challenging nuScenes test set while maintaining high efficiency. For the first time, the NDS score of a camera model reaches 60%.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection | DAIR-V2X-I | BEVDepth | AP|R40(easy) | 75.7 | #4 of 9 | Archive leaderboard | report |
| 3D Object Detection | DAIR-V2X-I | BEVDepth | AP|R40(hard) | 63.7 | #4 of 9 | Archive leaderboard | report |
| 3D Object Detection | DAIR-V2X-I | BEVDepth | AP|R40(moderate) | 63.6 | #4 of 9 | Archive leaderboard | report |
| 3D Object Detection | Rope3D | BEVDepth | AP@0.7 | 42.56 | #4 of 8 | Archive leaderboard | report |
| 3D Object Detection | nuScenes Camera Only | BEVDepth-pure | Future Frame | false | #13 of 19 | Archive leaderboard | report |
| 3D Object Detection | nuScenes Camera Only | BEVDepth-pure | NDS | 60.9 | #13 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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