  3D object detection from LiDAR data for autonomous driving has been making
remarkable strides in recent years. Among the state-of-the-art methodologies,
encoding point clouds into a bird's eye view (BEV) has been demonstrated to be
both effective and efficient. Different from perspective views, BEV preserves
rich spatial and distance information between objects. Yet, while farther
objects of the same type do not appear smaller in the BEV, they contain sparser
point cloud features. This fact weakens BEV feature extraction using
shared-weight convolutional neural networks (CNNs). In order to address this
challenge, we propose Range-Aware Attention Network (RAANet), which extracts
effective BEV features and generates superior 3D object detection outputs. The
range-aware attention (RAA) convolutions significantly improve feature
extraction for near as well as far objects. Moreover, we propose a novel
auxiliary loss for point density estimation to further enhance the detection
accuracy of RAANet for occluded objects. It is worth to note that our proposed
RAA convolution is lightweight and compatible to be integrated into any CNN
architecture used for detection from a BEV. Extensive experiments on the
nuScenes and KITTI datasets demonstrate that our proposed approach outperforms
the state-of-the-art methods for LiDAR-based 3D object detection, with
real-time inference speed of 16 Hz for the full version and 22 Hz for the lite
version tested on nuScenes lidar frames. The code is publicly available at our
Github repository https://github.com/erbloo/RAAN.
