Fuzziness Driven Adaptive Sampling for Monte Carlo Global Illuminated Rendering

  • Qing Xu
  • Mateu Sbert
  • Zhigeng Pan
  • Wei Wang
  • Lianping Xing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4035)


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.


Adaptive Sampling Global Illumination Image Synthesis Fuzzy Technique Test Scene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qing Xu
    • 1
  • Mateu Sbert
    • 2
  • Zhigeng Pan
    • 3
  • Wei Wang
    • 1
  • Lianping Xing
    • 1
  1. 1.Tianjin UniversityTianjinChina
  2. 2.University of GironaGironaSpain
  3. 3.Zhejiang UniversityHangzhouChina

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