  Efficient processing and feature extraction of largescale point clouds are
important in related computer vision and cyber-physical systems. This work
investigates point cloud resampling based on hypergraph signal processing
(HGSP) to better explore the underlying relationship among different cloud
points and to extract contour-enhanced features. Specifically, we design
hypergraph spectral filters to capture multi-lateral interactions among the
signal nodes of point clouds and to better preserve their surface outlines.
Without the need and the computation to first construct the underlying
hypergraph, our low complexity approach directly estimates hypergraph spectrum
of point clouds by leveraging hypergraph stationary processes from the observed
3D coordinates. Evaluating the proposed resampling methods with several
metrics, our test results validate the high efficacy of hypergraph
characterization of point clouds and demonstrate the robustness of
hypergraph-based resampling under noisy observations.
