矢量化储层采样

Shinji Ogaki
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引用次数: 3

摘要

储层采样正在成为实时渲染的重要组成部分,因为它可以在有限的存储空间内实现重要的重采样。Chao的加权随机抽样算法因其简单性而成为一种受欢迎的选择。虽然它很优雅,但有一个基本问题,即必须生成许多随机数来更新存储库。为了解决这个问题,我们对Chao的算法进行了样本扭曲的修改。我们以两种不同的方式应用样品翘曲,并对它们进行比较。我们进一步对改进的算法进行矢量化,使储层采样对CPU渲染更有用,并给出了几个实际的例子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vectorized Reservoir Sampling
Reservoir sampling is becoming an essential component of realtime rendering as it enables importance resampling with limited storage. Chao’s weighted random sampling algorithm is a popular choice because of its simplicity. Although it is elegant, there is a fundamental issue that many random numbers must be generated to update reservoirs. To address this issue, we modify Chao’s algorithm with sample warping. We apply sample warping in two different ways and compare them. We further vectorize the modified algorithm to make reservoir sampling more useful for CPU rendering and give a couple of practical examples.
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