Adaptive Sampling for Monte Carlo Global Illumination Using Tsallis Entropy

  • Qing Xu
  • Shiqiang Bao
  • Rui Zhang
  • Ruijuan Hu
  • Mateu Sbert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3802)


Adaptive sampling is an interesting tool to eliminate noise, which is one of the main problems of Monte Carlo global illumination algorithms. We investigate the Tsallis entropy to do adaptive sampling. Implementation results show that adaptive sampling based on Tsallis entropy consistently outperforms the counterpart based on Shannon entropy.


Shannon Entropy None None Adaptive Sampling Spectral Channel Entropic Index 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Qing Xu
    • 1
  • Shiqiang Bao
    • 1
  • Rui Zhang
    • 1
  • Ruijuan Hu
    • 1
  • Mateu Sbert
    • 2
  1. 1.Department of Computer Science and TechnologyTianjin UniversityChina
  2. 2.Institute of Informatics and ApplicationsUniversity of GironaSpain

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