Hypergraph Spectral Analysis and Processing in 3D Point Cloud
Hypergraph Spectral Analysis and Processing in 3D Point Cloud
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DOI：
10.1109/tip.2020.3042088
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发表时间：
2021-01-01
影响因子：
10.6
通讯作者：
Ding, Zhi
中科院分区：
文献类型：
--
作者：
Zhang, Songyang;Cui, Shuguang;Ding, Zhi
随着虚拟现实应用的日益普及，三维点云已经成为表征三维物体和环境的基本数据结构。为了有效地处理三维点云，一个适合底层结构和离群点噪声的模型总是至关重要的。在这项工作中，我们提出了一种基于超图的新的点云模型，该模型适合于高效的分析和处理。我们提出了一种基于张量的方法来估计理想和噪声环境下点云的超图频谱分量和频率系数。我们建立了超图频率和结构特征之间的解析联系。我们进一步评估了超图谱估计在两个常见的点云采样和去噪应用中的有效性，并针对这些应用给出了具体的超图滤波器设计和谱特性。实验结果表明，超图信号处理作为一种工具来表征三维点云的基本属性具有很强的能力。
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 applications of sampling and denoising of point clouds for which we provide specific hypergraph filter design and spectral properties. Experimental results demonstrate the strength of hypergraph signal processing as a tool in characterizing the underlying properties of 3D point clouds.