To exploit uncertainty masking for adaptive image rendering

For high-quality image rendering using Monte Carlo methods, a large number of samples are required to be computed for each pixel. Adaptive sampling aims to decrease the total number of samples by concentrating samples on difficult regions. However, existing adaptive sampling schemes haven't fully exploited the potential of image regions with complex structures to the reduction of sample numbers. To solve this problem, we propose to exploit uncertainty masking in adaptive sampling. Experimental results show that incorporation of uncertainty information leads to significant sample reduction and therefore time-savings.