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dc.contributor.authorSingh, Gurprit
dc.contributor.authorÖztireli, Cengiz
dc.contributor.authorAhmed, Abdalla G.M.
dc.contributor.authorCoeurjolly, David
dc.contributor.authorSubr, Kartic
dc.contributor.authorDeussen, Oliver
dc.contributor.authorOstromoukhov, Victor
dc.contributor.authorRamamoorthi, Ravi
dc.contributor.authorJarosz, Wojciech
dc.date.accessioned2019-08-01T13:57:22Z
dc.date.available2019-08-01T13:57:22Z
dc.date.issued2019-06-07
dc.identifier.citationSingh, G., Öztireli, C., Ahmed, A. G. M., Coeurjolly, D., Subr, K., Deussen, O., … Jarosz, W. (2019). Analysis of Sample Correlations for Monte Carlo Rendering. Computer Graphics Forum, 38(2), 473–491. doi:10.1111/cgf.13653
dc.identifier.doi10.1111/cgf.13653
dc.identifier.urihttp://hdl.handle.net/10754/656310
dc.description.abstractModern physically based rendering techniques critically depend on approximating integrals of high dimensional functions representing radiant light energy. Monte Carlo based integrators are the choice for complex scenes and effects. These integrators work by sampling the integrand at sample point locations. The distribution of these sample points determines convergence rates and noise in the final renderings. The characteristics of such distributions can be uniquely represented in terms of correlations of sampling point locations. Hence, it is essential to study these correlations to understand and adapt sample distributions for low error in integral approximation. In this work, we aim at providing a comprehensive and accessible overview of the techniques developed over the last decades to analyze such correlations, relate them to error in integrators, and understand when and how to use existing sampling algorithms for effective rendering workflows.
dc.description.sponsorshipWe are grateful to all the anonymous reviewers for their constructive remarks. This work was partially supported by the Fraunhofer and Max Planck cooperation program within the German pact for research and innovation (PFI). Kartic Subr was supported by a Royal Society University Research Fellowship, Ravi Ramamoorthi was supported by NSF grant 1451830 and Wojciech Jarosz was partially supported by NSF grant ISS-8127”96.
dc.publisherWiley
dc.relation.urlhttps://people.mpi-inf.mpg.de/~gsingh/2019-singh-star.html
dc.relation.urlhttp://diglib.eg.org/handle/10.1111/cgf13653
dc.relation.urlhttps://cs.dartmouth.edu/~wjarosz/publications/singh19analysis.html
dc.rightsArchived with thanks to Computer Graphics Forum
dc.titleAnalysis of Sample Correlations for Monte Carlo Rendering
dc.typeArticle
dc.contributor.departmentKAUST, Saudi Arabia
dc.identifier.journalComputer Graphics Forum
dc.rights.embargodate2020-05-01
dc.eprint.versionPost-print
dc.contributor.institutionMax-Planck Institute for Informatics, Saarbrücken
dc.contributor.institutionDisney Research, Zurich
dc.contributor.institutionUniversité de Lyon / CNRS, France
dc.contributor.institutionUniversity of Edinburgh, UK
dc.contributor.institutionUniversity of Konstanz, Germany
dc.contributor.institutionUniversity of California, San Diego, USA
dc.contributor.institutionDartmouth College, USA
kaust.personAhmed, Abdalla G.M.
dc.date.published-online2019-06-07
dc.date.published-print2019-05


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