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Optimizing Control Variate Estimators for Rendering
Author(s) -
Fan Shaohua,
Chenney Stephen,
Hu Bo,
Tsui KamWah,
Lai Yuchi
Publication year - 2006
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.1467-8659.2006.00954.x
Subject(s) - rendering (computer graphics) , computer science , estimator , control variates , random variate , importance sampling , monte carlo method , computer graphics , algorithm , graphics , artificial intelligence , computer graphics (images) , mathematical optimization , mathematics , statistics , hybrid monte carlo , random variable , markov chain monte carlo , bayesian probability
We present the Optimizing Control Variate (OCV) estimator, a new estimator for Monte Carlo rendering. Based upon a deterministic sampling framework, OCV allows multiple importance sampling functions to be combined in one algorithm. Its optimizing nature addresses a major problem with control variate estimators for rendering: users supply a generic correlated function which is optimized for each estimate, rather than a single highly tuned one that must work well everywhere. We demonstrate OCV with both direct lighting and irradiance‐caching examples, showing improvements in image error of over 35% in some cases, for little extra computation time. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism Color, shading, shadowing, and texture G.3 [Probability and Statistics]: Probabilistic AlgorithmsKeywords:direct lighting, deterministic mixture sampling, control variates