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Analysis of Sample Correlations for Monte Carlo Rendering
Author(s) -
Singh Gurprit,
Öztireli Cengiz,
Ahmed Abdalla G.M.,
Coeurjolly David,
Subr Kartic,
Deussen Oliver,
Ostromoukhov Victor,
Ramamoorthi Ravi,
Jarosz Wojciech
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13653
Subject(s) - rendering (computer graphics) , computer science , monte carlo method , integrator , importance sampling , sample (material) , algorithm , sampling (signal processing) , workflow , mathematical optimization , mathematics , computer graphics (images) , statistics , computer vision , physics , computer network , bandwidth (computing) , filter (signal processing) , database , thermodynamics
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.