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Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering
Author(s) -
Zwicker M.,
Jarosz W.,
Lehtinen J.,
Moon B.,
Ramamoorthi R.,
Rousselle F.,
Sen P.,
Soler C.,
Yoon S.E.
Publication year - 2015
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.12592
Subject(s) - computer science , a priori and a posteriori , rejection sampling , adaptive sampling , monte carlo method , rendering (computer graphics) , estimator , sampling (signal processing) , variance (accounting) , global illumination , importance sampling , algorithm , generality , artificial intelligence , filter (signal processing) , computer vision , markov chain monte carlo , hybrid monte carlo , mathematics , statistics , bayesian probability , psychology , philosophy , accounting , epistemology , business , psychotherapist
Abstract Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state‐of‐the‐art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real‐world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.