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DTSTART:18871231T000000
DTSTART:19881009T020000
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DTSTAMP:20230103T035307Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_250@linklings.com
SUMMARY:Marginal Multiple Importance Sampling
DESCRIPTION:Technical Papers\n\nMarginal Multiple Importance Sampling\n\nW
 est, Georgiev, Hachisuka\n\nMultiple importance sampling (MIS) is a powerf
 ul tool to combine different sampling techniques in a provably good manner
 . MIS requires that the techniques' probability density functions (PDFs) a
 re readily evaluable point-wise. However, this requirement may not be sati
 sfied when (some of) those PDFs are marginals, i.e., integrals of other PD
 Fs. We generalize MIS to combine samples from such marginal PDFs. The key 
 idea is to consider each marginalization domain as a continuous space of s
 ampling techniques with readily evaluable (conditional) PDFs. We stochasti
 cally select techniques from these spaces and combine the samples drawn fr
 om them into an unbiased estimator. Prior work has dealt with the special 
 cases of multiple classical techniques or a single marginal one. Our formu
 lation can handle mixtures of those.\n\nWe apply our marginal MIS formulat
 ion to light-transport simulation to demonstrate its utility. We devise a 
 marginal path sampling framework that makes previously intractable samplin
 g techniques practical and significantly broadens the path-sampling choice
 s beyond what is presently possible. We highlight results from two algorit
 hms based on marginal MIS: a novel formulation of path-space filtering at 
 multiple vertices along a camera path and a similar filtering method for p
 hoton-density estimation.\n\nRegistration Category: FULL ACCESS, EXPERIENC
 E PLUS ACCESS, EXPERIENCE ACCESS, TRADE EXHIBITOR\n\nLanguage: ENGLISH\n\n
 Format: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_250&sess=sess15
 3
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