Instance-aware Multi-Camera 3D Object Detection with Structural Priors
  Mining and Self-Boosting Learning
Yang Jiao Zequn Jie Shaoxiang Chen Lechao Cheng Jingjing Chen Lin Ma Yu-Gang Jiang
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.