Title :
Adaptive sampling with Renyi entropy in Monte Carlo path tracing
Author :
Xu, Qing ; Hu, Ruijuan ; Xing, Lianping ; Xu, Yuan
Author_Institution :
Sch. of Inf., Tianjin Univ.
Abstract :
Adaptive sampling is an interesting tool to lower noise, which is one of the main problems of Monte Carlo global illumination algorithms such as the famous and baseline Monte Carlo path tracing. The classic information measure, namely, Shannon entropy has been applied successfully for adaptive sampling in Monte Carlo path tracing. In this paper we investigate the generalized Renyi entropy to establish the refinement criteria to guide both pixel super sampling and pixel subdivision adaptively. Implementation results show that the adaptive sampling based on Renyi entropy outperforms the counterpart based on Shannon entropy consistently
Keywords :
computer animation; entropy; importance sampling; lighting; noise abatement; ray tracing; rendering (computer graphics); virtual reality; Monte Carlo global illumination algorithms; Monte Carlo path tracing; Renyi entropy; Shannon entropy; adaptive sampling; noise reduction; pixel subdivision; pixel super sampling; Cities and towns; Entropy; Humans; Image sampling; Lighting; Monte Carlo methods; Pixel; Rendering (computer graphics); Sampling methods; Signal to noise ratio;
Conference_Titel :
Signal Processing and Information Technology, 2005. Proceedings of the Fifth IEEE International Symposium on
Conference_Location :
Athens
Print_ISBN :
0-7803-9313-9
DOI :
10.1109/ISSPIT.2005.1577198