  Three-dimensional (3D) point clouds serve as an important data representation
for visualization applications. The rapidly growing utility and popularity of
point cloud processing strongly motivate a plethora of research activities on
large-scale point cloud processing and feature extraction. In this work, we
investigate point cloud resampling based on hypergraph signal processing
(HGSP). We develop a novel method to extract sharp object features and reduce
the size of point cloud representation. By directly estimating hypergraph
spectrum based on hypergraph stationary processing, we design a spectral
kernel-based filter to capture high-dimensional interactions among the signal
nodes of point clouds and to better preserve their surface outlines.
Experimental results validate the effectiveness of hypergraph in representing
point clouds, and demonstrate the robustness of the proposed resampling method
in noisy environment.
