Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings

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dc.contributor.authorBitterli, Benedikten_US
dc.contributor.authorRousselle, Fabriceen_US
dc.contributor.authorMoon, Bochangen_US
dc.contributor.authorIglesias-Guitian, Jose A.en_US
dc.contributor.authorAdler, Daviden_US
dc.contributor.authorMitchell, Kennyen_US
dc.contributor.authorJarosz, Wojciechen_US
dc.contributor.authorNovak, Janen_US
dc.contributor.editorElmar Eisemann and Eugene Fiumeen_US
dc.date.accessioned2016-06-17T14:10:39Z
dc.date.available2016-06-17T14:10:39Z
dc.date.issued2016en_US
dc.description.abstractWe address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state-of-the-art performance on a wide range of scenes. We analyze existing approaches from a theoretical and empirical point of view, relating the strengths and limitations of their corresponding components with an emphasis on production requirements. The observations of our analysis instruct the design of our new filter that offers high-quality results and stable performance. A key observation of our analysis is that using auxiliary buffers (normal, albedo, etc.) to compute the regression weights greatly improves the robustness of zero-order models, but can be detrimental to first-order models. Consequently, our filter performs a first-order regression leveraging a rich set of auxiliary buffers only when fitting the data, and, unlike recent works, considers the pixel color alone when computing the regression weights. We further improve the quality of our output by using a collaborative denoising scheme. Lastly, we introduce a general mean squared error estimator, which can handle the collaborative nature of our filter and its nonlinear weights, to automatically set the bandwidth of our regression kernel.en_US
dc.description.number4en_US
dc.description.sectionheadersFaster Renderingen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume35en_US
dc.identifier.doi10.1111/cgf.12954en_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages107-117en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12954en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.7 [Computer Graphics]en_US
dc.subjectThree Dimensional Graphics and Realismen_US
dc.subjectRaytracingen_US
dc.subjecten_US
dc.subjectI.4.3 [Computer Graphics]en_US
dc.subjectEnhancementen_US
dc.subjectFilteringen_US
dc.titleNonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderingsen_US
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