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TZID:Australia/Brisbane
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DTSTART:19920301T030000
TZOFFSETFROM:+1100
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BEGIN:VEVENT
DTSTAMP:20191203T080637Z
LOCATION:Great Hall 1&2
DTSTART;TZID=Australia/Brisbane:20191117T180200
DTEND;TZID=Australia/Brisbane:20191117T180300
UID:siggraphasia_SIGGRAPH Asia 2019_sess222_papers_449@linklings.com
SUMMARY:Selectively Metropolised Monte Carlo light transport simulation
DESCRIPTION:Technical Papers Fast-Forward \n\nSelectively Metropolised Mon
 te Carlo light transport simulation\n\nBitterli, Jarosz\n\nLight transport
  is a complex problem with many solutions. Practitioners are now faced wit
 h the difficult task of choosing which rendering algorithm to use for any 
 given scene. Simple Monte Carlo methods, such as path tracing, work well f
 or the majority of lighting scenarios, but introduce excessive variance wh
 en they encounter transport they cannot sample (such as caustics). More so
 phisticated rendering algorithms, such as bidirectional path tracing, hand
 le a larger class of light transport robustly, but have a high computation
 al overhead that makes them inefficient for scenes that are not dominated 
 by difficult transport. The underlying problem is that rendering algorithm
 s can only be executed indiscriminately on all transport, even though they
  may only offer improvement for a subset of paths. In this paper, we intro
 duce a new scheme for selectively combining different Monte Carlo renderin
 g algorithms. We use a simple transport method (e.g. path tracing) as the 
 base, and treat high variance \n"fireflies" as seeds for a Markov chain th
 at locally uses a Metropolised version of a more sophisticated transport m
 ethod for exploration, removing the firefly in an unbiased manner. We use 
 a weighting scheme inspired by MIS to partition the integrand into regions
  the base method can sample well and those it cannot, and only use Metropo
 lis for the latter. This constrains the Markov chain to paths where it off
 ers improvement, and keeps it away from regions already handled well by th
 e base estimator. Combined with stratified initialization, short chain len
 gths and careful allocation of samples, this vastly reduces non-uniform no
 ise and temporal flickering artifacts normally encountered with a global a
 pplication of Metropolis methods. Through careful design choices, we ensur
 e our algorithm never performs much worse than the base estimator alone, a
 nd usually performs significantly better, thereby reducing the need to exp
 eriment with different algorithms for each scene.\n\nRegistration Category
 : Platinum Pass, Full Conference Pass, Full Conference One-Day Pass, Basic
  Conference Pass, Student One-Day Pass, Exhibitor Pass
URL:https://sa2019.conference-program.com/presentation?id=papers_449&sess=
 sess222
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