Population Monte Carlo Path Tracing
Yu-Chi Lai, ShaoHua Fan, Charcles Dyer
We present a novel global illumination algorithm which distributes more image samples on regions with perceptually high variance. Our algorithm iterates on a population of pixel positions used to estimate the intensity of each pixel in the image. A member kernel function, which automatically adapts to approximate the target ditribution by using the information collected in previous iterations, is responsible for proposing a new sample position from the current one during the mutation process. The kernel function is designed to explore a proper area around the population sample to reduce the local variance. The resampling process eliminates samples located in the low-variance or well-explored regions and generates new samples to achieve ergocity. New samples are generated by considering two factors: the perceptual variance and the stratification of the sample distributions on the image plane. Our results show that the visual quality of the rendered image can be improved by exploring the correlated information among image samples.
Download this report (PDF)
Return to tech report index