An Efficient Hypergraph Approach to Robust Point Cloud Resampling

PubDate: February 2022

Teams: University of California at Davis

Writers: Qinwen Deng; Songyang Zhang; Zhi Ding

PDF: An Efficient Hypergraph Approach to Robust Point Cloud Resampling

Abstract

Efficient processing and feature extraction of large-scale 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 points in the point cloud and to extract contour-enhanced features. Specifically, we design hypergraph spectral filters to capture multilateral 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.