Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. Under such a paradigm, accurate BEV representation construction relies on reliable depth estimation for multi-camera images. However, existing approaches exhaustively predict depths for every pixel without prioritizing objects, which are precisely the entities requiring detection in the 3D space. To this end, we propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector. First, a category-specific structural priors mining approach is proposed for enhancing the efficacy of monocular depth generation. Besides, a self-boosting learning strategy is further proposed to encourage the model to place more emphasis on challenging objects in computation-expensive temporal stereo matching. Together they provide advanced depth estimation results for high-quality BEV features construction, benefiting the ultimate 3D detection. The proposed method achieves state-of-the-art performances on the challenging nuScenes benchmark, and extensive experimental results demonstrate the effectiveness of our designs.
@article{arxiv.2312.08004,
title = {Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning},
author = {Yang Jiao and Zequn Jie and Shaoxiang Chen and Lechao Cheng and Jingjing Chen and Lin Ma and Yu-Gang Jiang},
journal= {arXiv preprint arXiv:2312.08004},
year = {2023}
}