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.

提出了一种新方法 IA-BEV，它将图像平面实例感知集成到基于 BEV 的深度估计过程中，通过增强单目深度生成的有效性和鼓励模型在计算密集的时间立体匹配中更加注重具有挑战性的物体，为高质量 BEV 特征构建提供先进的深度估计结果，从而有利于最终的三维检测。所提出的方法在具有挑战性的 nuScenes 基准测试中取得了最先进的性能，广泛的实验结果表明了我们设计的有效性。