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

Ying Hui Wang; Wei Yong Wu

doi:10.4028/www.scientific.net/AMM.201-202.753

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 201-202A Graph Spectral Smoothing Approach for...

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

Abstract:

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. We construct an undirected weighted graph from scattered points, and 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 works well on complex models with large scale point sets.

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Periodical:

Applied Mechanics and Materials (Volumes 201-202)

Pages:

753-757

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.201-202.753

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Online since:

October 2012

Authors:

Ying Hui Wang, Wei Yong Wu

Keywords:

Graph Spectrum, Graphic Processing Unit (GPU), Point-Sampled, Smoothing

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