Density-based Outlier Rejection in Monte Carlo Rendering

dc.contributor.authorDeCoro, Christopheren_US
dc.contributor.authorWeyrich, Timen_US
dc.contributor.authorRusinkiewicz, Szymonen_US
dc.date.accessioned2015-02-23T17:37:01Z
dc.date.available2015-02-23T17:37:01Z
dc.date.issued2010en_US
dc.description.abstractThe problem of noise in Monte-Carlo rendering arising from estimator variance is well-known and well-studied. In this work, we concentrate on identifying individual light paths as outliers that lead to significant spikes of noise and represent a challenge for existing filtering methods. Most noise-reduction methods, such as importance sampling and stratification, attempt to generate samples that are expected a priori to have lower variance, but do not take into account actual sample values. While these methods are essential to decrease overall noise, we show that filtering samples a posteriori allows for greater reduction of spiked noise. In particular, given evaluated sample values, outliers can be identified and removed. Conforming with conventions in statistics, we emphasize that the term outlier should not be taken as synonymous with incorrect , but as referring to samples that distort the empirically-observed distribution of energy relative to the true underlying distribution. By expressing a path distribution in joint image and color space, we show how outliers can be characterized by their density across the set of all nearby paths in this space. We show that removing these outliers leads to significant improvements in rendering quality.en_US
dc.description.number7en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume29en_US
dc.identifier.doi10.1111/j.1467-8659.2010.01799.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages2119-2125en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2010.01799.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltden_US
dc.titleDensity-based Outlier Rejection in Monte Carlo Renderingen_US
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