Hypergraph Spectral Analysis and Processing in 3D Point Cloud

Along with increasingly popular virtual reality applications, the three-dimensional (3D) point cloud has become a fundamental data structure to characterize 3D objects and surroundings. To process clouds efficiently, suitable model for underlying outlier noises is always critical. In this work, we propose hypergraph-based new that amenable efficient analysis processing. We introduce tensor-based methods estimate hypergraph spectrum components frequency coefficients of in both ideal noisy settings. establish an analytical connection between frequencies structural features. further evaluate efficacy estimation two common applications sampling denoising which provide specific filter design spectral properties. Experimental results demonstrate strength signal processing as tool characterizing properties clouds.