AM-GM difference based adaptive sampling for Monte Carlo global illumination

作者：Xu Qing^*; Sbert Mateu; Feixas Miquel; Zhang Jianfeng

来源：International Conference on Computational Science and Its Applications (ICCSA 2007), 2007-08-26 to 2007-08-29.

• homogeneity measure
• adaptive sampling
• Monte Carlo
• global illumination

Monte Carlo is the only choice for a physically correct method to do global illumination in the field of realistic image synthesis. Generally Monte Carlo based algorithms require a lot of time to eliminate the noise to get an acceptable image. Adaptive sampling is an interesting tool to reduce noise, in which the evaluation of homogeneity of pixel's samples is the key point. In this paper, we propose a new homogeneity measure, namely the arithmetic mean - geometric mean difference (abbreviated to AM - GM difference), which is developed to execute adaptive sampling efficiently. Implementation results demonstrate that our novel adaptive sampling method can perform significantly better than classic ones.

• 出版日期2007
• 单位天津大学