On Learning the Best Local Balancing Strategy
Loading...
Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
@inproceedings{10.2312:egs.20201009,
booktitle = {Eurographics 2020 - Short Papers},
editor = {Wilkie, Alexander and Banterle, Francesco},
title = {{On Learning the Best Local Balancing Strategy}},
author = {Murray, David and Benzait, Sofiane and Pacanowski, Romain and Granier, Xavier},
year = {2020},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-101-4},
DOI = {10.2312/egs.20201009}
}