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Practical Product Sampling by Fitting and Composing Warps
Author(s) -
Hart D.,
Pharr M.,
Müller T.,
Lopes W.,
McGuire M.,
Shirley P.
Publication year - 2020
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.14060
Subject(s) - sampling (signal processing) , rendering (computer graphics) , computer science , importance sampling , sample (material) , slice sampling , rejection sampling , monte carlo method , bilinear interpolation , product (mathematics) , algorithm , computer graphics (images) , mathematics , statistics , computer vision , markov chain monte carlo , hybrid monte carlo , geometry , chemistry , filter (signal processing) , chromatography
We introduce a Monte Carlo importance sampling method for integrands composed of products and show its application to rendering where direct sampling of the product is often difficult. Our method is based on warp functions that operate on the primary samples in [0,1) n , where each warp approximates sampling a single factor of the product distribution. Our key insight is that individual factors are often well‐behaved and inexpensive to fit and sample in primary sample space, which leads to a practical, efficient sampling algorithm. Our sampling approach is unbiased, easy to implement, and compatible with multiple importance sampling. We show the results of applying our warps to projected solid angle sampling of spherical triangles, to sampling bilinear patch light sources, and to sampling glossy BSDFs and area light sources, with efficiency improvements of over 1.6× on real‐world scenes.