Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

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dc.contributor.authorZwicker, Matthiasen_US
dc.contributor.authorJarosz, Wojciechen_US
dc.contributor.authorLehtinen, Jaakkoen_US
dc.contributor.authorMoon, Bochangen_US
dc.contributor.authorRamamoorthi, Ravien_US
dc.contributor.authorRousselle, Fabriceen_US
dc.contributor.authorSen, Pradeepen_US
dc.contributor.authorSoler, Cyrilen_US
dc.contributor.authorYoon, Sungeui E.en_US
dc.contributor.editorK. Hormann and O. Staadten_US
dc.date.accessioned2015-04-16T06:15:22Z
dc.date.available2015-04-16T06:15:22Z
dc.date.issued2015en_US
dc.description.abstractMonte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.en_US
dc.description.documenttypestar
dc.description.number2en_US
dc.description.sectionheadersState of the Art Reportsen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12592en_US
dc.identifier.pages667-681en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12592en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectDisplay algorithmsen_US
dc.titleRecent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Renderingen_US
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