BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:18871231T000000
DTSTART:19881009T020000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
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_409@linklings.com
SUMMARY:Learning-based Inverse Rendering of Complex Indoor Scenes with Dif
 ferentiable Monte Carlo Raytracing
DESCRIPTION:Technical Papers\n\nLearning-based Inverse Rendering of Comple
 x Indoor Scenes with Differentiable Monte Carlo Raytracing\n\nZhu, Luan, H
 uo, Lin, Zhong...\n\nIndoor scenes typically exhibit complex, spatially-va
 rying appearance from global illumination, making the inverse rendering a 
 challenging ill-posed problem. This work presents an end-to-end, learning-
 based inverse rendering framework incorporating differentiable Monte Carlo
  raytracing with importance sampling. The framework takes a single image a
 s input to jointly recover the underlying geometry, spatially-varying ligh
 ting, and photorealistic materials. Specifically, we introduce a physicall
 y-based differentiable rendering layer with screen-space ray tracing, resu
 lting in more realistic specular reflections that match the input photo. I
 n addition, we create a large-scale, photorealistic indoor scene dataset w
 ith significantly richer details like complex furniture and dedicated deco
 rations. Further, we design a novel out-of-view lighting network with unce
 rtainty-aware refinement leveraging hypernetwork-based neural radiance fie
 lds to predict lighting outside the view of the input photo. Through exten
 sive evaluations on common benchmark datasets, we demonstrate superior inv
 erse rendering quality of our method compared to state-of-the-art baseline
 s, enabling various applications such as complex object insertion and mate
 rial editing with high fidelity. Code and data will be made available at h
 ttps://jingsenzhu.github.io/invrend.\n\nRegistration Category: FULL ACCESS
 , EXPERIENCE PLUS ACCESS, EXPERIENCE ACCESS, TRADE EXHIBITOR\n\nLanguage: 
 ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_409&sess=sess15
 3
END:VEVENT
END:VCALENDAR