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    Analysis of Sample Correlations for Monte Carlo Rendering

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    Type
    Article
    Authors
    Singh, Gurprit
    Öztireli, Cengiz
    Ahmed, Abdalla G.M.
    Coeurjolly, David
    Subr, Kartic
    Deussen, Oliver
    Ostromoukhov, Victor
    Ramamoorthi, Ravi
    Jarosz, Wojciech
    KAUST Department
    KAUST, Saudi Arabia
    Date
    2019-06-07
    Online Publication Date
    2019-06-07
    Print Publication Date
    2019-05
    Embargo End Date
    2020-05-01
    Permanent link to this record
    http://hdl.handle.net/10754/656310
    
    Metadata
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    Abstract
    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.
    Citation
    Singh, 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
    Sponsors
    We 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.
    Publisher
    Blackwell Publishing Ltd
    Journal
    Computer Graphics Forum
    DOI
    10.1111/cgf.13653
    Additional Links
    https://people.mpi-inf.mpg.de/~gsingh/2019-singh-star.html
    http://diglib.eg.org/handle/10.1111/cgf13653
    https://cs.dartmouth.edu/~wjarosz/publications/singh19analysis.html
    ae974a485f413a2113503eed53cd6c53
    10.1111/cgf.13653
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