Visual Computing

University of Konstanz
Computer Graphics Forum (Proceedings of Eurographics - State of the Art Reports)

Analysis of sample correlations for Monte Carlo rendering

G. Singh, C. Öztireli, A. Ahmed, D. Coeurjolly, K. Subr, O. Deussen, V. Ostromoukhov, R. Ramamoorthi, W. Jarosz
Teaser of Analysis of sample correlations for Monte Carlo rendering

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Paper (.pdf, 21.5 MB)

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.

BibTeX

@article{Singh2019Analysissamplecorrelations,
  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.},
  author     = {G. Singh and C. Öztireli and A. Ahmed and D. Coeurjolly and K. Subr and O. Deussen and V. Ostromoukhov and R. Ramamoorthi and W. Jarosz},
  doi        = {10/gf6rzc},
  issn       = {1467-8659},
  journal    = {Computer Graphics Forum (Proceedings of Eurographics - State of the Art Reports)},
  month      = {may},
  number     = {2},
  pages      = {473--491},
  title      = {Analysis of sample correlations for Monte Carlo rendering},
  volume     = {38},
  year       = {2019},
  url        = {http://graphics.uni-konstanz.de/publikationen/Singh2019Analysissamplecorrelations},
}