A Graph Spectral Smoothing Approach for Point-based Surface on GPU

摘要：

In order to smooth point-based surface with noise, Bezier surface fitting method was applied to compute principal curvature and normal vector at each point firstly. Secondly, we construct an undirected weighted graph from scattered points, and then a heat diffusion partial differential equation was defined on this graph. Considering principal curvature as heat power, the smoothing process is realized by diffusing principal curvature along graph structure. We adapt the position of each point along normal direction according to the difference of curvature. Computing heat diffusion equation can be boiled down to the spectral decomposition of Laplacian matrix. We use parallel computing method to solve the spectral decomposition of Laplacian Matrix on GPU to improve the efficiency. Some examples show that our method word well on complex models with large scale points set.

关键词： Smoothing Graph spectrum Point-Sampled GPU

作者: Wu Weiyong Wang Yinghui

作者单位: School of Information science & Technology Jiujiang University Jiujiang, China School of Mechanical & Material Engineering Jiujiang University Jiujiang, China

会议类型: 国际会议

会议名称: 2010 International Conference on Information Security and Artificial Intelligence(2010年信息安全与人工智能国际会议 ISAI 2010)

会议地点: 成都

会议语种:英文

页码: 469-472

在线出版日期: 2010-12-17（万方平台首次上网日期，不代表论文的发表时间）