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 this https URL

提出了一种基于点密度感知的体素网络（PDV）方法，通过使用核密度估计和具有点密度位置编码的自注意力，通过聚合体素特征来解决 LiDAR 的点密度变化对 3D 物体检测精度的影响。实验结果表明，PDV 在 Waymo 开放数据集上优于所有最先进的方法，在 KITTI 数据集上取得了竞争性的结果。