Abstract:
Monte Carlo is a good choice to compute the problem of global illumination in the field of realistic image synthesis. However, the image produced by a Monte Carlo-based global illumination algorithm is noisy when not using a large enough number of samples. Adaptive sampling is an attractive means to reduce these noises. This paper introduces a new measure based on entropy for adaptive sampling. Experimental results show that the method can perform better than classic ones.
Key words:
global illumination,
Monte Carlo,
adaptive sampling,
entropy
摘要: 在真实感图形生成领域里,蒙特卡罗方法是计算整体光照问题的极佳选择。但是,在用基于蒙特卡罗的全局光照算法生成的图像中,当没有足够多的采样量的时候,存在大量的噪声。自适应抽样方法是减少这种噪声的一种很好的方法。该文提出了一种新的基于信息熵的自适应抽样算法。实验结果表明,该方法的效果优于香农信息熵等经典方法。
关键词:
整体光照,
蒙特卡罗,
自适应抽样,
信息熵
CLC Number:
XING Lian-ping; XU Qing. Adaptive Sampling for Monte Carlo Global Illumination Based on Entropy[J]. Computer Engineering, 2007, 33(21): 219-221,.
邢连萍;徐 庆. 基于信息熵的蒙特卡罗全局光照的自适应抽样[J]. 计算机工程, 2007, 33(21): 219-221,.