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DTSTAMP:20260817T171532Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241206T090000
DTEND;TZID=Asia/Tokyo:20241206T091100
UID:siggraphasia_SIGGRAPH Asia 2024_sess140_papers_422@linklings.com
SUMMARY:Differentiating Variance for Variance-Aware Inverse Rendering
DESCRIPTION:Kai Yan (University of California Irvine, Wētā FX); Vincent Pe
 goraro, Marc Droske, and Jiří Vorba (Wētā FX); and Shuang Zhao (University
  of California Irvine)\n\nMonte Carlo methods have been widely adopted in 
 physics-based rendering.\n    A key property of a Monte Carlo estimator is
  its variance, which dictates the convergence rate of the estimator.\n    
 In this paper, we devise a mathematical formulation for derivatives of ren
 dering variance with respect to not only scene parameters (e.g., surface r
 oughness) but also sampling probabilities.\n    Based on this formulation,
  we introduce unbiased Monte Carlo estimators for those derivatives.\n    
 Our theory and algorithm enable variance-aware inverse rendering which alt
 ers a virtual scene and/or an estimator in an optimal way to offer a good 
 balance between bias and variance.\n    We evaluate our technique using se
 veral synthetic examples.\n\nRegistration Category: Full Access, Full Acce
 ss Supporter\n\nLanguage Format: English Language\n\nSession Chair: Seungy
 ong Lee (POSTECH)\n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_422&sess=sess140
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