BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Asia/Tokyo
X-LIC-LOCATION:Asia/Tokyo
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:JST
DTSTART:18871231T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241203T163000
DTEND;TZID=Asia/Tokyo:20241203T164100
UID:siggraphasia_SIGGRAPH Asia 2024_sess110_papers_669@linklings.com
SUMMARY:MARS: Multi-sample Allocation through Russian roulette and Splitti
 ng
DESCRIPTION:Technical Papers\n\nJoshua Meyer, Alexander Rath, and Ömercan 
 Yazici (Saarland Informatics Campus) and Philipp Slusallek (German Researc
 h Center for Artificial Intelligence, Saarland Informatics Campus)\n\nMult
 iple importance sampling (MIS) is an indispensable tool in rendering that 
 constructs robust sampling strategies by combining the respective strength
 s of individual distributions. Its efficiency can be greatly improved by c
 arefully selecting the number of samples drawn from each distribution, but
  automating this process remains a challenging problem. Existing works are
  mostly limited to mixture sampling, in which only a single sample is draw
 n in total, and the works that do investigate multi-sample MIS only optimi
 ze the sample counts at a per-pixel level, which cannot account for variat
 ions beyond the first bounce. Recent work on Russian roulette and splittin
 g has demonstrated how fixed-point schemes can be used to spatially vary s
 ample counts to optimize image efficiency but is limited to choosing the s
 ame number of samples across all sampling strategies. Our work proposes a 
 highly flexible sample allocation strategy that bridges the gap between th
 ese areas of work. We show how to iteratively optimize the sample counts t
 o maximize the efficiency of the rendered image using a lightweight data s
 tructure, which allows us to make local and individual decisions per techn
 ique. We demonstrate the benefits of our approach in two applications, pat
 h guiding and bidirectional path tracing, in both of which we achieve cons
 istent and substantial speedups over the respective previous state-of-the-
 art.\n\nRegistration Category: Full Access, Full Access Supporter\n\nLangu
 age Format: English Language\n\nSession Chair: Michael Wimmer (TU Wien)
URL:https://asia.siggraph.org/2024/program/?id=papers_669&sess=sess110
END:VEVENT
END:VCALENDAR