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Metropolis Iteration for Global Illumination.

Authors: Szirmay-Kalos, László; Benedek, Balázs; Sbert, Mateu;

Metropolis Iteration for Global Illumination.

Abstract

This paper presents a stochastic iteration algorithm solving the global illumination problem, where the random sampling is governed by classical importance sampling and also by the Metropolis method. Point pairs where radiance transfer takes place are obtained with random ray shooting. Ray shooting can mimic the source radiance and the geometric factor, but not the receiving capability of the target (i.e. the BRDF and the area), which results in not optimal importance sampling. This deficiency is attacked by the Metropolis method. The pseudo random numbers controlling ray shooting are generated not independently, but by the perturbation of the previously used pseudo random numbers. These perturbations are accepted or rejected according to the change of the contribution of the transfers. The algorithm is mesh based, requires only a few variables per patch, and can render moderately complex glossy scenes in a few seconds.

Country
Czech Republic
Keywords

technika konečných prvků, finite element technique, global illumination, globální osvětlení, Monte Carlo

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green