On Learning the Best Local Balancing Strategy

Murray, David; Benzait, Sofiane; Pacanowski, Romain; Granier, Xavier

On Learning the Best Local Balancing Strategy

dc.contributor.author	Murray, David	en_US
dc.contributor.author	Benzait, Sofiane	en_US
dc.contributor.author	Pacanowski, Romain	en_US
dc.contributor.author	Granier, Xavier	en_US
dc.contributor.editor	Wilkie, Alexander and Banterle, Francesco	en_US
dc.date.accessioned	2020-05-24T13:42:28Z
dc.date.available	2020-05-24T13:42:28Z
dc.date.issued	2020
dc.description.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.	en_US
dc.description.sectionheaders	Rendering II + Shape
dc.description.seriesinformation	Eurographics 2020 - Short Papers
dc.identifier.doi	10.2312/egs.20201009
dc.identifier.isbn	978-3-03868-101-4
dc.identifier.issn	1017-4656
dc.identifier.pages	25-28
dc.identifier.uri	https://doi.org/10.2312/egs.20201009
dc.identifier.uri	https://diglib.eg.org:443/handle/10.2312/egs20201009
dc.publisher	The Eurographics Association	en_US
dc.rights	Attribution 4.0 International License
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	]
dc.title	On Learning the Best Local Balancing Strategy	en_US