Point Cloud Resampling via Hypergraph Signal Processing
Point Cloud Resampling via Hypergraph Signal Processing
复制标题
基于超图信号处理的点云重构
DOI：
10.1109/lsp.2021.3119257
复制
发表时间：
2021-02
影响因子：
3.9
通讯作者：
Qinwen Deng;Songyang Zhang-;Zhi Ding
中科院分区：
文献类型：
--
作者：
Qinwen Deng;Songyang Zhang-;Zhi Ding
三维（3D）点云是可视化应用中的重要数据表示形式。点云处理的实用性和普及程度的快速增长，极大地推动了大量关于大规模点云处理和特征提取的研究活动。在这项工作中，我们研究了基于超图信号处理（HGSP）的点云重采样。我们开发了一种新的方法来提取清晰的物体特征并减少点云表示的数据量。通过基于超图平稳处理直接估计超图谱，我们设计了一种基于谱核的滤波器，以捕捉点信号节点之间的高维相互作用，并更好地保留物体表面轮廓。实验结果验证了超图在表示点云中的有效性，并证明了所提算法在噪声环境下的鲁棒性。
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.