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Adaptive Sampling for Monte Carlo Global Illumination Using Tsallis Entropy

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Computational Intelligence and Security (CIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3802))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tianjin University, China

    Qing Xu, Shiqiang Bao, Rui Zhang & Ruijuan Hu

  2. Institute of Informatics and Applications, University of Girona, Spain

    Mateu Sbert

Authors
  1. Qing Xu
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  2. Shiqiang Bao
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  3. Rui Zhang
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  4. Ruijuan Hu
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  5. Mateu Sbert
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Editor information

Editors and Affiliations

  1. Microelectronic Instiute, Xidian University, 710071, Xi’an, China

    Yue Hao

  2. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  3. School of Computer Science and Technology, Xidian University, Xi’an, China

    Yu-Ping Wang

  4. Department of Computer Science, Hong Kong Baptist University, Hong Kong,  

    Yiu-ming Cheung

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  6. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  7. Key Laboratory of Computer Networks and Information Security(Ministry of Education), Xidian University, 710071, Xi’an, China

    Jianfeng Ma

  8. National Laboratory of Antennas and Microwave Technology, Xidian University, 710071, Xi’an, Shanxi, P.R. China

    Yong-Chang Jiao

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© 2005 Springer-Verlag Berlin Heidelberg

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Xu, Q., Bao, S., Zhang, R., Hu, R., Sbert, M. (2005). Adaptive Sampling for Monte Carlo Global Illumination Using Tsallis Entropy. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596981_147

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  • DOI: https://doi.org/10.1007/11596981_147

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30819-5

  • Online ISBN: 978-3-540-31598-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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