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Adaptive sampling based on fuzzy inference

Published:29 November 2006Publication History

ABSTRACT

Monte Carlo is the only choice for a physically correct method to do global illumination in the field of realistic image rendering. Adaptive sampling is an appealing means to reduce noise, which is resulted from the general Monte Carlo global illumination algorithms. In this paper, we take advantage of fuzzy rule-based reasoning to achieve different refinement thresholds for different pixels in the synthesized image. The developed technique can do adaptive sampling elaborately and effectively. Extensive implementation results indicate that our novel method can achieve significantly better than classic ones. To our knowledge, this is the first application of the fuzzy inference to global illumination.

References

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        cover image ACM Conferences
        GRAPHITE '06: Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia
        November 2006
        489 pages
        ISBN:1595935649
        DOI:10.1145/1174429

        Copyright © 2006 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 29 November 2006

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        Acceptance Rates

        GRAPHITE '06 Paper Acceptance Rate47of83submissions,57%Overall Acceptance Rate124of241submissions,51%

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