BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
X-LIC-LOCATION:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:19911015T033000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20251218T030657Z
LOCATION:Meeting Room S426+S427\, Level 4
DTSTART;TZID=Asia/Hong_Kong:20251216T105000
DTEND;TZID=Asia/Hong_Kong:20251216T110100
UID:siggraphasia_SIGGRAPH Asia 2025_sess118_papers_2326@linklings.com
SUMMARY:Statistical Error Reduction for Monte Carlo Rendering
DESCRIPTION:Hiroyuki Sakai, Christian Freude, Michael Wimmer, and David Ha
 hn (Technische Universität Wien (TU Wien))\n\nDenoising is an important po
 st-processing step in physically based Monte Carlo (MC) rendering. While n
 eural networks are widely used in practice, statistical analysis has recen
 tly become a viable alternative for denoising. In this paper, we present a
  general framework for statistics-based error reduction of both estimated 
 radiance and variance. Specifically, we introduce a novel denoising approa
 ch for variance estimates, which can either improve variance-aware adaptiv
 e sampling or provide additional input for image denoising in a cascaded m
 anner. Furthermore, we present multi-transform denoising: a general and ef
 ficient correction scheme for non-normal distributions, which typically oc
 cur in MC rendering. All these contributions combine to a robust denoising
  pipeline that does not require any pretraining and can run efficiently on
  current GPU hardware. Our results show distinct advantages over previous 
 denoising methods, especially in the range of a few hundred samples per pi
 xel, which is of high practical relevance. Finally, we demonstrate good co
 nvergence behavior as the number of samples increases, providing predictab
 le results with low bias that are free of hallucinated neural artifacts. I
 n summary, our statistics-based algorithms for adaptive sampling and denoi
 sing deliver fast, consistent, low-bias variance and radiance estimates.\n
 \nRegistration Category: Full Access, Full Access Supporter\n\nSession Cha
 ir: Oliver Deussen (University of Konstanz)\n\n
END:VEVENT
END:VCALENDAR