1. BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
- Author
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Li, Yinhao, Ge, Zheng, Yu, Guanyi, Yang, Jinrong, Wang, Zengran, Shi, Yukang, Sun, Jianjian, and Li, Zeming
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
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%., Comment: Accepted by AAAI2023
- Published
- 2022