Robust Sample Budget Allocation for MIS

dc.contributor.author	Szirmay-Kalos, László	en_US
dc.contributor.author	Sbert, Mateu	en_US
dc.contributor.editor	Pelechano, Nuria	en_US
dc.contributor.editor	Vanderhaeghe, David	en_US
dc.date.accessioned	2022-04-22T08:16:10Z
dc.date.available	2022-04-22T08:16:10Z
dc.date.issued	2022
dc.identifier.isbn	978-3-03868-169-4
dc.identifier.issn	1017-4656
dc.identifier.uri	https://doi.org/10.2312/egs.20221022
dc.identifier.uri	https://diglib.eg.org:443/handle/10.2312/egs20221022
dc.description.abstract	Multiple Importance Sampling (MIS) combines several sampling techniques. Its weighting scheme depends on how many samples are generated with each particular method. This paper examines the optimal determination of the number of samples allocated to each of the combined techniques taking into account that this decision can depend only on a relatively small number of previous samples. The proposed method is demonstrated with the combination of BRDF sampling and Light source sampling, and we show that due to its robustness, it can outperform the theoretically more accurate approaches.	en_US
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	Robust Sample Budget Allocation for MIS	en_US
dc.description.seriesinformation	Eurographics 2022 - Short Papers
dc.description.sectionheaders	Rendering and Illumination
dc.identifier.doi	10.2312/egs.20221022
dc.identifier.pages	17-20
dc.identifier.pages	4 pages

Except where otherwise noted, this item's license is described as Attribution 4.0 International License