基于超图信号处理的点云重构
Point Cloud Resampling Through Hypergraph Signal Processing
摘要：
三维（3D）点云是可视化应用中的重要数据表示。点云处理的快速增长的效用和普及强烈地激发了大量的研究活动，对大规模点云处理和特征提取。本文研究了基于超图信号处理的点云重构方法。我们开发了一种新的方法来提取尖锐的对象的功能和减少点云表示的数据大小。通过基于超图平稳处理直接估计超图谱，设计了一种基于谱核的滤波器来捕捉点信号节点之间的高维交互，更好地保持物体表面轮廓。实验结果验证了超图表示点云的有效性，并证明了该算法对噪声的鲁棒性。
英文摘要：
Three-dimensional (3D) point clouds are important data representations in visualization applications. The rapidly growing utility and popularity of point cloud processing strongly motivate a plethora of research activities on large-scale point cloud processing and feature extraction. In this work, we investigate point cloud resampling based on hypergraph signal processing (HGSP). We develop a novel method to extract sharp object features and reduce the data size of point cloud representation. By directly estimating hypergraph spectrum based on hypergraph stationary processing, we design a spectral kernel-based filter to capture high-dimensional interactions among point signal nodes and to better preserve object surface outlines. Experimental results validate the effectiveness of hypergraph in representing point clouds, and demonstrate the robustness of the proposed algorithm under noise.
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