![Eg2019](/img/e4eb1931-3d0f-45b4-bdcb-43947833e6c3/eg2019.png?fm=jpg&q=80&fit=max&crop=720%2C432%2C0%2C0)
Gurprit Singh1, Cengiz Öztireli2, Abdalla GM 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
4Université de Lyon / CNRS, France 5University of Edinburgh, UK 6University of Konstanz, Germany
7University of California, San Diego, USA 8Dartmouth College, USA
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 correlationsof 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.
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