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arXiv · 2206.10092

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
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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%.

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Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, Zeming Li. 2022-06-21. BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection. https://arxiv.org/abs/2206.10092

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