Orthogonal Array Sampling for Monte Carlo Rendering

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dc.contributor.authorJarosz, Wojciechen_US
dc.contributor.authorEnayet, Afnanen_US
dc.contributor.authorKensler, Andrewen_US
dc.contributor.authorKilpatrick, Charlieen_US
dc.contributor.authorChristensen, Peren_US
dc.contributor.editorBoubekeur, Tamy and Sen, Pradeepen_US
dc.date.accessioned2019-07-14T19:24:36Z
dc.date.available2019-07-14T19:24:36Z
dc.date.issued2019
dc.description.abstractWe generalize N-rooks, jittered, and (correlated) multi-jittered sampling to higher dimensions by importing and improving upon a class of techniques called orthogonal arrays from the statistics literature. Renderers typically combine or ''pad'' a collection of lower-dimensional (e.g. 2D and 1D) stratified patterns to form higher-dimensional samples for integration. This maintains stratification in the original dimension pairs, but looses it for all other dimension pairs. For truly multi-dimensional integrands like those in rendering, this increases variance and deteriorates its rate of convergence to that of pure random sampling. Care must therefore be taken to assign the primary dimension pairs to the dimensions with most integrand variation, but this complicates implementations. We tackle this problem by developing a collection of practical, in-place multi-dimensional sample generation routines that stratify points on all t-dimensional and 1-dimensional projections simultaneously. For instance, when t=2, any 2D projection of our samples is a (correlated) multi-jittered point set. This property not only reduces variance, but also simplifies implementations since sample dimensions can now be assigned to integrand dimensions arbitrarily while maintaining the same level of stratification. Our techniques reduce variance compared to traditional 2D padding approaches like PBRT's (0,2) and Stratified samplers, and provide quality nearly equal to state-of-the-art QMC samplers like Sobol and Halton while avoiding their structured artifacts as commonly seen when using a single sample set to cover an entire image. While in this work we focus on constructing finite sampling point sets, we also discuss potential avenues for extending our work to progressive sequences (more suitable for incremental rendering) in the future.en_US
dc.description.number4
dc.description.sectionheadersSampling
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume38
dc.identifier.doi10.1111/cgf.13777
dc.identifier.issn1467-8659
dc.identifier.pages135-147
dc.identifier.urihttps://doi.org/10.1111/cgf.13777
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13777
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectComputer graphics
dc.subjectRay tracing
dc.subjectTheory of computation
dc.subjectGenerating random combinatorial structures
dc.subjectMathematics of computing
dc.subjectStochastic processes
dc.subjectComputations in finite fields
dc.titleOrthogonal Array Sampling for Monte Carlo Renderingen_US
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