Abstract
Monte Carlo is the only choice for a physically correct method to do global illumination in the field of realistic image synthesis. Generally Monte Carlo based algorithms require a lot of time to eliminate the noise to get an acceptable image. Adaptive sampling is an interesting tool to reduce noise, in which the evaluation of homogeneity of pixel’s samples is the key point. In this paper, we propose a new homogeneity measure, namely the arithmetic mean - geometric mean difference (abbreviated to AM − GM difference), which is developed to execute adaptive sampling efficiently. Implementation results demonstrate that our novel adaptive sampling method can perform significantly better than classic ones.
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© 2007 Springer-Verlag Berlin Heidelberg
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Xu, Q., Sbert, M., Feixas, M., Zhang, J. (2007). AM-GM Difference Based Adaptive Sampling for Monte Carlo Global Illumination. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74477-1_56
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DOI: https://doi.org/10.1007/978-3-540-74477-1_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74475-7
Online ISBN: 978-3-540-74477-1
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