P-RPF: pixel-based random parameter filtering for Monte Carlo rendering

Abstract

In this paper we propose Pixel-based Random Parameter Filtering (P-RPF) for efficiently denoising images generated from complex illuminations with a high sample count. We design various operations of our method to have time complexity that is independent from the number of samples per pixel. We compute feature weights by measuring the functional relationships between MC inputs and output in a sample basis. To accelerate this sample-basis process we propose to use an up sampling method for feature weights. We have applied our method to a wide variety of models with different rendering effects. Our method runs significantly faster than the original RPF, while maintaining visually pleasing and numerically similar results. As a result, our method shows more visually pleasing and numerically better results than RPF in an equal-time comparison.