  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%.
