AM-GM Difference Based Adaptive Sampling for Monte Carlo Global Illumination

  • Qing Xu
  • Mateu Sbert
  • Miquel Feixas
  • Jianfeng Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4706)

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.

Keywords

Homogeneity measure Adaptive Sampling Monte Carlo Global Illumination 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Qing Xu
    • 1
  • Mateu Sbert
    • 2
  • Miquel Feixas
    • 2
  • Jianfeng Zhang
    • 1
  1. 1.Tianjin University, Tianjin 300072China
  2. 2.University of Girona, Girona 17003Spain

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