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DTSTAMP:20260114T163633Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_574@linklings.com
SUMMARY:Multiple-bounce Smith Microfacet BRDFs using the Invariance Princi
 ple
DESCRIPTION:Yuang Cui (Anhui Science and Technology University); Gaole Pan
  and Jian Yang (Nanjing University of Science and Technology); Lei Zhang (
 The Hong Kong Polytechnic University); Ling-Qi Yan (University of Californ
 ia, University of California Santa Barbara); and Beibei Wang (Nankai Unive
 rsity, Nanjing University of Science and Technology)\n\nSmith microfacet m
 odels are widely used in computer graphics to represent materials. Traditi
 onal microfacet models do not consider the multiple bounces on microgeomet
 ries, leading to visible energy missing, especially on rough surfaces. Lat
 er, as the equivalence between the microfacets and volume has been reveale
 d, random walk solutions have been proposed to introduce multiple bounces,
  but at the cost of high variance. Recently, the position-free property ha
 s been introduced into the multiple-bounce model, resulting in much less n
 oise, but also bias or a complex derivation. In this paper, we propose a s
 imple way to derive the multiple-bounce Smith microfacet bidirectional ref
 lectance distribution functions (BRDFs) using the invariance principle. At
  the core of our model is a shadowing-masking function for a path consisti
 ng of direction collections, rather than separated bounces. Our model ensu
 res unbiasedness and can produce less noise compared to the previous work 
 with equal time, thanks to the simple formulation. Furthermore, we also pr
 opose a novel probability density function (PDF) for BRDF multiple importa
 nce sampling, which has a better match with the multiple-bounce BRDFs, pro
 ducing less noise than previous naive approximations.\n\nRegistration Cate
 gory: Full Access, Enhanced Access, Trade Exhibitor, Experience Hall Exhib
 itor\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_574&sess=sess209
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