Adaptive sampling with Renyi entropy in Monte Carlo path tracing

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