GPU中局部细分算法的分析与实现

Gustavo N. Nunes, Rodrigo Braga, Alexandre Valdetaro, A. Raposo, B. Feijó
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引用次数: 1

摘要

能够在不牺牲内存带宽的情况下优化网格是一个强大的工具,可以通过图形管道的新Tessellator部分进行探索。在本文中,我们提供了一个详细的实现两种局部细分算法- pn -三角形和Phong Tessellation -使用新的Tessellator管道。此外,还对两种算法进行了明确的质量和性能比较。我们的结果表明,当在当前的Tessellation硬件中实现时,Phong Tessellation与pn - triangle相比具有相当大的性能提升。然而,pn -三角形算法的视觉质量在视觉上更平滑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis and Implementation of Local Subdivision Algorithms in the GPU
Being able to refine a mesh without comprimising the memory bandwidth is a powerful tool that might be explored with the new Tessellator part of the graphics pipeline. In this paper we provide a detailed implementation of two local subdivision algorithms - PN-Triangles and Phong Tessellation - using the new Tessellator pipeline. Moreover, a explicit quality and performance comparison between both algorithms is made. Our results showed that Phong Tessellation has a considerable performance gain in comparison with PN-Triangles when implemented in the current Tessellation hardware. However, the visual quality of PN-Triangles algorithm is visually smoother.
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