Deterministic Importance Sampling with Error Diffusion

This paper proposes a deterministic importance sampling algorithm that is based on the recognition that delta‐sigma modulation is equivalent to importance sampling. We propose a generalization for delta‐sigma modulation in arbitrary dimensions, taking care of the curse of dimensionality as well. Unl...

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Bibliographic Details
Published inComputer graphics forum Vol. 28; no. 4; pp. 1055 - 1064
Main Authors Szirmay-Kalos, László, Szécsi, László
Format Journal Article
LanguageEnglish
Published Oxford, UK Blackwell Publishing Ltd 01.06.2009
Subjects
Algorithms
Computer graphics
Computer science
Sampling
Sampling techniques
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Summary:This paper proposes a deterministic importance sampling algorithm that is based on the recognition that delta‐sigma modulation is equivalent to importance sampling. We propose a generalization for delta‐sigma modulation in arbitrary dimensions, taking care of the curse of dimensionality as well. Unlike previous sampling techniques that transform low‐discrepancy and highly stratified samples in the unit cube to the integration domain, our error diffusion sampler ensures the proper distribution and stratification directly in the integration domain. We also present applications, including environment mapping and global illumination rendering with virtual point sources.
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2009.01482.x