  The recent trend for multi-camera 3D object detection is through the unified
bird's-eye view (BEV) representation. However, directly transforming features
extracted from the image-plane view to BEV inevitably results in feature
distortion, especially around the objects of interest, making the objects blur
into the background. To this end, we propose OA-BEV, a network that can be
plugged into the BEV-based 3D object detection framework to bring out the
objects by incorporating object-aware pseudo-3D features and depth features.
Such features contain information about the object's position and 3D
structures. First, we explicitly guide the network to learn the depth
distribution by object-level supervision from each 3D object's center. Then, we
select the foreground pixels by a 2D object detector and project them into 3D
space for pseudo-voxel feature encoding. Finally, the object-aware depth
features and pseudo-voxel features are incorporated into the BEV representation
with a deformable attention mechanism. We conduct extensive experiments on the
nuScenes dataset to validate the merits of our proposed OA-BEV. Our method
achieves consistent improvements over the BEV-based baselines in terms of both
average precision and nuScenes detection score. Our codes will be published.
