Analysis of Sample Correlations for Monte Carlo Rendering

Gurprit Singh1, Cengiz Öztireli2, Abdalla G.M. Ahmed3, David Coeurjolly4,
Kartic Subr5, Oliver Deussen6, Victor Ostromoukhov4, Ravi Ramamoorthi7, Wojciech Jarosz8
1Max Planck Institute for Informatics, Saarbrücken, 2Disney Research, Zurich, 3KAUST, Saudi Arabia, 4UniversiteĢ de Lyon / CNRS, France, 5University of Edinburgh, UK, 6University of Konstanz, Germany, 7University of California, San Diego, USA, 8Dartmouth College, USA
In Computer Graphics Forum (Proceedings of Eurographics - State of the art Reports) 2019
Snow

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.

Material

Paper

EG Presentation:
Part 1: Introduction (Gurprit) PDF   PDF with notes   Keynote
Part 2: Sampling Measures and Error Formulations (Cengiz) PDF   Powerpoint
Part 3: Error Analysis of Common Sampling Strategies (Gurprit) PDF   PDF with notes   Keynote

Acknowledgements

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-1812796.

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