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Sequential Monte Carlo Adaptation in Low‐Anisotropy Participating Media
Author(s) -
Pegoraro Vincent,
Wald Ingo,
Parker Steven G.
Publication year - 2008
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/j.1467-8659.2008.01247.x
Subject(s) - control variates , computer science , rendering (computer graphics) , monte carlo method , variance reduction , importance sampling , algorithm , radiance , theoretical computer science , computer engineering , mathematical optimization , artificial intelligence , hybrid monte carlo , mathematics , statistics , markov chain monte carlo , physics , optics , bayesian probability
This paper presents a novel method that effectively combines both control variates and importance sampling in a sequential Monte Carlo context. The radiance estimates computed during the rendering process are cached in a 5D adaptive hierarchical structure that defines dynamic predicate functions for both variance reduction techniques and guarantees well‐behaved PDFs, yielding continually increasing efficiencies thanks to a marginal computational overhead. While remaining unbiased, the technique is effective within a single pass as both estimation and caching are done online, exploiting the coherency in illumination while being independent of the actual scene representation. The method is relatively easy to implement and to tune via a single parameter, and we demonstrate its practical benefits with important gains in convergence rate and competitive results with state of the art techniques.