2019
DOI: 10.1111/cgf.13775
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Quantifying the Error of Light Transport Algorithms

Abstract: This paper proposes a new methodology for measuring the error of unbiased physically based rendering algorithms. The current state of the art includes mean squared error (MSE) based metrics and visual comparisons of equal‐time renderings of competing algorithms. Neither is satisfying as MSE does not describe behavior and can exhibit significant variance, and visual comparisons are inherently subjective. Our contribution is two‐fold: First, we propose to compute many short renderings instead of a single long ru… Show more

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Cited by 4 publications
(2 citation statements)
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“…It is similar to the fact that gradient‐domain methods tend to produce lower‐frequency errors than the primal counterparts [HGP∗19]. An error metric that can fully capture such a local correlation of pixels is still an open problem even with recent work by Celarek et al [CJWL19], especially since it involves human perception.…”
Section: Resultsmentioning
confidence: 94%
“…It is similar to the fact that gradient‐domain methods tend to produce lower‐frequency errors than the primal counterparts [HGP∗19]. An error metric that can fully capture such a local correlation of pixels is still an open problem even with recent work by Celarek et al [CJWL19], especially since it involves human perception.…”
Section: Resultsmentioning
confidence: 94%
“…Numerous variance reduction algorithms like multiple importance sampling [Veach 1998], and control variates [Loh 1995;Glasserman 2004] improve the error convergence of light transport renderings. Recently, Celarek et al [2019] proposed error spectrum ensemble (ESE), a tool for measuring the distribution of error over frequencies. ESE reveals correlation between pixels and can be used to detect outliers, which offset the amount of error substantially.…”
Section: Quantitative Error Assessment In Renderingmentioning
confidence: 99%