Adaptive sampling for monte carlo global illumination using tsallis entropy

Authors:
Qing Xu;Shiqiang Bao;Rui Zhang;Ruijuan Hu;Mateu Sbert
Affiliations:
Department of Computer Science and Technology, Tianjin University, China;Department of Computer Science and Technology, Tianjin University, China;Department of Computer Science and Technology, Tianjin University, China;Department of Computer Science and Technology, Tianjin University, China;Institute of Informatics and Applications, University of Girona, Spain
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Year:
2005

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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.