基于超图谱聚类的点云分割
Point Cloud Segmentation based on Hypergraph Spectral Clustering
摘要：
超图谱分析是数据分析中处理复杂数据结构的有效工具。三维点云的表面及其点之间的多边关系可以通过高维超边自然地捕获。本文研究了超图谱分析在三维点云无监督分割中的应用。我们从观测点云坐标估计和排序超图谱。通过基于谱分量强度对估计的超图谱空间进行冗余修剪，提出了一种基于聚类的分割方法。我们将所提出的方法应用于各种点云，并分析其各自的光谱特性。我们的实验结果表明，所提出的分割方法的有效性和效率。
英文摘要：
Hypergraph spectral analysis has emerged as an effective tool processing complex data structures in data analysis. The surface of a three-dimensional (3D) point cloud and the multilateral relationship among their points can be naturally captured by the high-dimensional hyperedges. This work investigates the power of hypergraph spectral analysis in unsupervised segmentation of 3D point clouds. We estimate and order the hypergraph spectrum from observed point cloud coordinates. By trimming the redundancy from the estimated hypergraph spectral space based on spectral component strengths, we develop a clustering-based segmentation method. We apply the proposed method to various point clouds, and analyze their respective spectral properties. Our experimental results demonstrate the effectiveness and efficiency of the proposed segmentation method.
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