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DTSTAMP:20240214T070245Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T154500
DTEND;TZID=Australia/Melbourne:20231213T155500
UID:siggraphasia_SIGGRAPH Asia 2023_sess168_papers_546@linklings.com
SUMMARY:Extended Path Space Manifolds for Physically Based Differentiable 
 Rendering
DESCRIPTION:Technical Papers\n\nJiankai Xing and Xuejun Hu (Tsinghua Unive
 rsity), Fujun Luan (Adobe Research), Ling-Qi Yan (University of California
  Santa Barbara), and Kun Xu (Tsinghua University)\n\nPhysically based diff
 erentiable rendering has become an increasingly important topic in recent 
 years. A common pipeline computes local color derivatives of light paths o
 r pixels with respect to arbitrary scene parameters, and enables optimizin
 g or recovering the scene parameters through iterative gradient descent by
  minimizing the difference between rendered and target images. However, ex
 isting approaches cannot robustly handle complex illumination effects incl
 uding reflections, refractions, caustics, shadows, and highlights, especia
 lly when the initial and target locations of such illumination effects are
  not close to each other in the image space.\nTo address this problem, we 
 propose a novel data structure named extended path space manifolds. The ma
 nifolds are defined in the combined space of path vertices and scene param
 eters. By enforcing geometric constraints, the path vertices could be impl
 icitly and uniquely determined by perturbed scene parameters. This enables
  the manifold to track specific illumination effects and the corresponding
  paths, i.e., specular paths will still be specular paths after scene para
 meters are perturbed. Besides, the path derivatives with respect to scene 
 parameters could be computed by solving small linear systems. \nWe further
  propose a physically based differentiable rendering method built upon the
  theoretical results of extended path space manifolds. By incorporating th
 e path derivatives computed from the manifolds and an optimal transport ba
 sed loss function, our method is demonstrated to be more effective and rob
 ust than state-of-the-art approaches in inverse rendering applications inv
 olving complex illumination effects.\n\nRegistration Category: Full Access
 \n\nSession Chair: Soo-Mi Choi (Sejong University)
URL:https://asia.siggraph.org/2023/full-program?id=papers_546&sess=sess168
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