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DTSTAMP:20240214T070245Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T161000
DTEND;TZID=Australia/Melbourne:20231213T162500
UID:siggraphasia_SIGGRAPH Asia 2023_sess168_papers_269@linklings.com
SUMMARY:Amortizing Samples in Physics-Based Inverse Rendering using ReSTIR
DESCRIPTION:Technical Papers\n\nYu-Chen Wang (University of California Irv
 ine), Chris Wyman and Lifan Wu (NVIDIA), and Shuang Zhao (University of Ca
 lifornia Irvine)\n\nRecently, great progress has been made in physics-base
 d differentiable rendering. Existing differentiable rendering techniques t
 ypically focus on static scenes, but during inverse rendering—a key applic
 ation for differentiable rendering—the scene is updated dynamically by eac
 h gradient step. In this paper, we take a first step to leverage temporal 
 data in the context of inverse direct illumination. By adopting reservoir-
 based spatiotemporal resampled importance resampling (ReSTIR), we introduc
 e new Monte Carlo estimators for both interior and boundary components of 
 differential direct illumination integrals. We also integrate ReSTIR with 
 antithetic sampling to further improve its effectiveness. At equal frame t
 ime, our methods produce gradient estimates with up to 100× lower relative
  error than baseline methods. Additionally, we propose an inverse-renderin
 g pipeline that incorporates these estimators and provides reconstructions
  with up to 20× lower error.\n\nRegistration Category: Full Access\n\nSess
 ion Chair: Soo-Mi Choi (Sejong University)
URL:https://asia.siggraph.org/2023/full-program?id=papers_269&sess=sess168
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