论文标题
基于超晶光谱群集的点云分割
Point Cloud Segmentation based on Hypergraph Spectral Clustering
论文作者
论文摘要
HyperGraph光谱分析已成为数据分析中的有效工具处理复杂的数据结构。三维（3D）点云的表面及其点之间的多边关系可以由高维超过的超级中心自然捕获。这项工作研究了3D点云的无监督分割中超晶光谱分析的力量。我们从观察到的点云坐标中估算并订购了超图谱。通过基于频谱组件强度从估计的超图光谱空间中裁定冗余，我们开发了一种基于聚类的分割方法。我们将提出的方法应用于各种点云，并分析它们各自的光谱特性。我们的实验结果证明了所提出的分割方法的有效性和效率。
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.
