Analysis of Sample Correlations for Monte Carlo Rendering

Gurprit Singh, Cengiz Oztireli, Abdalla Ahmed, David Coeurjolly, Kartic Subr, Oliver Deussen, Victor Ostromoukhov, Ravi Ramamoorthi, Wojciech Jarosz

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)473-491
Number of pages19
JournalComputer Graphics Forum
Issue number2
Publication statusPublished - 7 Jun 2019
Event40th Annual Conference of the European Association for Computer Graphics - Genoa, Italy
Duration: 6 May 201910 May 2019

Keywords / Materials (for Non-textual outputs)

  • Mathematics of computing
  • Computation of transforms
  • stochastic processes
  • Number-theoretic computations
  • Com-puting methodologies
  • Ray tracing


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