On Learning the Best Local Balancing Strategy

D. Murray, S. Benzait, R. Pacanowski, Xavier Granier
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引用次数: 2

Abstract

Fast computation of light propagation using Monte Carlo techniques requires finding the best samples from the space of light paths. For the last 30 years, numerous strategies have been developed to address this problem but choosing the best one is really scene-dependent. Multiple Importance Sampling (MIS) emerges as a potential generic solution by combining different weighted strategies, to take advantage of the best ones. Most recent work have focused on defining the best weighting scheme. Among them, two paper have shown that it is possible, in the context of direct illumination, to estimate the best way to balance the number of samples between two strategies, on a per-pixel basis. In this paper, we extend this previous approach to Global Illumination and to three strategies.
关于最佳局部平衡策略的学习
利用蒙特卡罗技术进行光传播的快速计算需要从光路空间中找到最佳样本。在过去的30年里,已经开发了许多策略来解决这个问题,但选择最好的策略实际上取决于场景。多重重要性抽样(Multiple Importance Sampling, MIS)是一种潜在的通用解决方案,它结合了不同的加权策略,以利用最佳策略。最近的工作集中在确定最佳加权方案上。其中,有两篇论文表明,在直接照明的情况下,有可能在每个像素的基础上估计两种策略之间平衡样本数量的最佳方法。在本文中,我们将之前的方法扩展到全局照明和三种策略。
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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