@article{Singh2019-06-07Analy-46366,
  year={2019},
  doi={10.1111/cgf.13653},
  title={Analysis of Sample Correlations for Monte Carlo Rendering},
  number={2},
  volume={38},
  issn={0167-7055},
  journal={Computer Graphics Forum},
  pages={473--491},
  author={Singh, Gurprit and Öztireli, Cengiz and Ahmed, Abdalla G.M. and Coeurjolly, David and Subr, Kartic and Deussen, Oliver and Ostromoukhov, Victor and Ramamoorthi, Ravi and Jarosz, Wojciech}
}

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    <dc:creator>Ahmed, Abdalla G.M.</dc:creator>
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    <dc:creator>Jarosz, Wojciech</dc:creator>
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    <dc:contributor>Ostromoukhov, Victor</dc:contributor>
    <dc:contributor>Ramamoorthi, Ravi</dc:contributor>
    <dc:contributor>Deussen, Oliver</dc:contributor>
    <dc:creator>Ostromoukhov, Victor</dc:creator>
    <dc:contributor>Coeurjolly, David</dc:contributor>
    <dc:creator>Öztireli, Cengiz</dc:creator>
    <dc:contributor>Öztireli, Cengiz</dc:contributor>
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    <dc:contributor>Ahmed, Abdalla G.M.</dc:contributor>
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    <dcterms:abstract xml:lang="eng">Modern 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.</dcterms:abstract>
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