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Optimally Combining Sampling Techniques for Monte Carlo Rendering (1995)
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BibTeX
@MISC{Veach95optimallycombining,
author = {Eric Veach and Leonidas J. Guibas},
title = { Optimally Combining Sampling Techniques for Monte Carlo Rendering},
year = {1995}
}
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Abstract
Monte Carlo integration is a powerful technique for the evaluation of difficult integrals. Applications in rendering include distribution ray tracing, Monte Carlo path tracing, and form-factor computation for radiosity methods. In these cases variance can often be significantly reduced by drawing samples from several distributions, each designed to sample well some difficult aspect of the integrand. Normally this is done by explicitly partitioning the integration domain into regions that are sampled differently. We present a powerful alternative for constructing robust Monte Carlo estimators, by combining samples from several distributions in a way that is provably good. These estimators are unbiased, and can reduce variance significantly at little additional cost. We present experiments and measurements from several areas in rendering: calculation of glossy highlights from area light sources, the “final gather” pass of some radiosity algorithms, and direct solution of the rendering equation using bidirectional path tracing.
Keyphrases
monte carlo rendering several distribution radiosity method robust monte carlo estimator little additional cost glossy highlight monte carlo path tracing several area radiosity algorithm monte carlo integration difficult integral area light source form-factor computation integration domain present experiment difficult aspect direct solution final gather pas include distribution ray tracing bidirectional path tracing case variance powerful technique
