Error Analysis of Estimators that use Combinations of Stochastic Sampling Strategies for Direct Illumination

dc.contributor.authorSubr, Karticen_US
dc.contributor.authorNowrouzezahrai, Dereken_US
dc.contributor.authorJarosz, Wojciechen_US
dc.contributor.authorKautz, Janen_US
dc.contributor.authorMitchell, Kennyen_US
dc.contributor.editorWojciech Jarosz and Pieter Peersen_US
dc.date.accessioned2015-03-03T12:40:23Z
dc.date.available2015-03-03T12:40:23Z
dc.date.issued2014en_US
dc.description.abstractWe present a theoretical analysis of error of combinations of Monte Carlo estimators used in image synthesis. Importance sampling and multiple importance sampling are popular variance-reduction strategies. Unfortunately, neither strategy improves the rate of convergence of Monte Carlo integration. Jittered sampling (a type of stratified sampling), on the other hand is known to improve the convergence rate. Most rendering software optimistically combine importance sampling with jittered sampling, hoping to achieve both. We derive the exact error of the combination of multiple importance sampling with jittered sampling. In addition, we demonstrate a further benefit of introducing negative correlations (antithetic sampling) between estimates to the convergence rate. As with importance sampling, antithetic sampling is known to reduce error for certain classes of integrands without affecting the convergence rate. In this paper, our analysis and experiments reveal that importance and antithetic sampling, if used judiciously and in conjunction with jittered sampling, may improve convergence rates. We show the impact of such combinations of strategies on the convergence rate of estimators for direct illumination.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12416en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12416en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleError Analysis of Estimators that use Combinations of Stochastic Sampling Strategies for Direct Illuminationen_US
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