GPU上的广义Voronoi图计算

Zhan Yuan, Guodong Rong, X. Guo, Wenping Wang
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引用次数: 14

摘要

研究了利用GPU计算线段和曲线等高阶点的广义Voronoi图(GVD)的问题。该问题在许多领域都有应用,包括计算机动画、模式识别等。人们提出了许多利用GPU来加快GVD计算速度的方法。跳泛洪算法(称为JFA)就是这样一种高效的基于gpu的方法,特别适合计算点位置的普通Voronoi图。我们改进了跳泛洪算法,并将其应用于GVD的计算。具体来说,我们不像原来的JFA那样直接传播一个站点的完整信息(即坐标或其他几何参数),而是将站点信息存储在一个一维纹理中,并在另一个二维纹理中仅传播站点的短整数id来生成Voronoi图。这个简单的策略避免了存储冗余数据,并且与使用原始JFA相比,使用更少的内存可以更准确地计算GVD,而运行时间只会适度增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalized Voronoi Diagram Computation on GPU
We study the problem of using the GPU to compute the generalized Voronoi diagram (GVD) for higher-order sites, such as line segments and curves. This problem has applications in many fields, including computer animation, pattern recognition and so on. A number of methods have been proposed that use the GPU to speed up the computation of the GVD. The jump flooding algorithm (to be called JFA) is such an efficient GPU-based method that is particularly suitable for computing the ordinary Voronoi diagram of point sites. We improve the jump flooding algorithm and apply it to computing the GVD. Specifically, instead of directly propagating the complete information of a site (i.e. the coordinates or other geometric parameters) as in the original JFA, we store the site information in a 1-D texture, and propagate only the IDs, which are short integers, of the sites in another 2D texture to generate the Voronoi diagram. This simple strategy avoids storing redundant data and leads to considerately more accurate computation of the GVD with much less memory than using the original JFA, with only moderate increase of the running time.
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