  Without a shape-aware response, it is hard to characterize the 3D geometry of
a point cloud efficiently with a compact set of kernels. In this paper, we
advocate the use of Hausdorff distance as a shape-aware distance measure for
calculating point convolutional responses. The technique we present, coined
Hausdorff Point Convolution (HPC), is shape-aware. We show that HPC constitutes
a powerful point feature learning with a rather compact set of only four types
of geometric priors as kernels. We further develop a HPC-based deep neural
network (HPC-DNN). Task-specific learning can be achieved by tuning the network
weights for combining the shortest distances between input and kernel point
sets. We also realize hierarchical feature learning by designing a multi-kernel
HPC for multi-scale feature encoding. Extensive experiments demonstrate that
HPC-DNN outperforms strong point convolution baselines (e.g., KPConv),
achieving 2.8% mIoU performance boost on S3DIS and 1.5% on SemanticKITTI for
semantic segmentation task.
