  LiDAR has become one of the primary 3D object detection sensors in autonomous
driving. However, LiDAR's diverging point pattern with increasing distance
results in a non-uniform sampled point cloud ill-suited to discretized
volumetric feature extraction. Current methods either rely on voxelized point
clouds or use inefficient farthest point sampling to mitigate detrimental
effects caused by density variation but largely ignore point density as a
feature and its predictable relationship with distance from the LiDAR sensor.
Our proposed solution, Point Density-Aware Voxel network (PDV), is an
end-to-end two stage LiDAR 3D object detection architecture that is designed to
account for these point density variations. PDV efficiently localizes voxel
features from the 3D sparse convolution backbone through voxel point centroids.
The spatially localized voxel features are then aggregated through a
density-aware RoI grid pooling module using kernel density estimation (KDE) and
self-attention with point density positional encoding. Finally, we exploit
LiDAR's point density to distance relationship to refine our final bounding box
confidences. PDV outperforms all state-of-the-art methods on the Waymo Open
Dataset and achieves competitive results on the KITTI dataset. We provide a
code release for PDV which is available at https://github.com/TRAILab/PDV.
