Abstract
Monte Carlo is the only choice for a physically correct method to compute the problem of global illumination in the field of realistic image synthesis. Adaptive sampling is an interesting means to reduce noise, which is one of the major problems of general Monte Carlo global illumination algorithms. In this paper, we make use of the fuzzy uncertainty existing in image synthesis and exploit the formal concept of fuzziness in fuzzy set theory to evaluate pixel quality to run adaptive sampling efficiently. Experimental results demonstrate that our novel method can perform significantly better than classic ones. To our knowledge, this is the first application of the fuzzy technique to global illumination image synthesis problems.
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Xu, Q., Sbert, M., Pan, Z., Wang, W., Xing, L. (2006). Fuzziness Driven Adaptive Sampling for Monte Carlo Global Illuminated Rendering. In: Nishita, T., Peng, Q., Seidel, HP. (eds) Advances in Computer Graphics. CGI 2006. Lecture Notes in Computer Science, vol 4035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11784203_13
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DOI: https://doi.org/10.1007/11784203_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35638-7
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