论文标题
3D点云中的超晶光谱分析和处理
Hypergraph Spectral Analysis and Processing in 3D Point Cloud
论文作者
论文摘要
除了越来越流行的虚拟现实应用程序外，三维（3D）点云已成为表征3D对象和周围环境的基本数据结构。为了有效地处理3D点云，一个适合基础结构和离群噪声的合适模型始终至关重要。在这项工作中，我们提出了一个基于超图的新点云模型，该模型适合有效的分析和处理。我们介绍了基于张量的方法来估计理想和嘈杂设置中点云的超图谱和频率系数。我们在超图频率和结构特征之间建立了分析联系。我们进一步评估了在两个公共点云的应用中，我们还详细阐述了特定的特定的超图滤波器设计和光谱特性，并评估了超图谱估计的功效。经验性能证明了HyperGraph信号处理作为3D点云和基础特性中的工具的强度。
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 3D point clouds efficiently, a suitable model for the underlying structure and outlier noises is always critical. In this work, we propose a hypergraph-based new point cloud model that is amenable to efficient analysis and processing. We introduce tensor-based methods to estimate hypergraph spectrum components and frequency coefficients of point clouds in both ideal and noisy settings. We establish an analytical connection between hypergraph frequencies and structural features. We further evaluate the efficacy of hypergraph spectrum estimation in two common point cloud applications of sampling and denoising for which also we elaborate specific hypergraph filter design and spectral properties. The empirical performance demonstrates the strength of hypergraph signal processing as a tool in 3D point clouds and the underlying properties.
