Parallelization of Mesh Contraction and Fairing using OpenCL

Martin Madaras, R. Durikovic
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引用次数: 1

Abstract

We propose a parallel method for computing local Laplacian curvature flows for triangular meshes. Laplace operator is widely used in mesh processing for mesh fairing, noise removal or curvature estimation. If the Laplacian flow is used in global sense constraining a whole mesh with an iterative weighted linear system, it can be used even for mesh contraction. However, numerical solution of such a global linear system is computationally expensive. Therefore, we have developed a method to compute such an iterative linear system using only local neighbourhoods of each vertex in parallel. Parallel computation of local linear systems is performed on GPU using OpenCL. We have evaluated speedups of the parallelization using both local and global Laplacian flows. We show test cases, where the parallel local method can be used for mesh fairing. In contrary, we also investigate and outline a fail case, where the local Laplacian flow cannot be used. When the local Laplacian flow has problems with global convergence, we offer a global parallelization of the linear system solving as an alternative.
基于OpenCL的网格收缩和整流罩并行化
提出了一种计算三角形网格局部拉普拉斯曲率流的并行方法。拉普拉斯算子广泛应用于网格处理中,用于网格整流、去噪或曲率估计。如果将拉普拉斯流用于全局意义上约束一个具有迭代加权线性系统的整体网格,它甚至可以用于网格收缩。然而,这种全局线性系统的数值解在计算上是非常昂贵的。因此,我们开发了一种方法来计算这样一个迭代线性系统,只使用每个顶点的局部邻域并行。利用OpenCL在GPU上实现了局部线性系统的并行计算。我们使用局部和全局拉普拉斯流对并行化的加速进行了评估。我们展示了测试用例,其中并行局部方法可用于网格整流罩。相反,我们也调查和概述了一个失败的情况,其中局部拉普拉斯流不能使用。当局部拉普拉斯流存在全局收敛问题时,我们提供了线性系统解的全局并行化方法作为替代。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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