Bidirectional Estimators for Light Transport

Abstract

Most of the research on the global illumination problem in computer graphics has been concentrated on finite-element (radiosity) techniques. Monte Carlo methods are an intriguing alternative which are attractive for their ability to handle very general scene descriptions without the need for meshing. In this paper we study techniques for reducing the sampling noise inherent in pure Monte Carlo approaches to global illumination. Every light energy transport path from a light source to the eye can be generated in a number of different ways, according to how we partition the path into an initial portion traced from a light source, and a final portion traced from the eye. Each partitioning gives us a different unbiased estimator, but some partitionings give estimators with much lower variance than others. We give examples of this phenomenon and describe its significance. We also present work in progress on the problem of combining these multiple estimators to achieve near-optimal variance, with the goal of producing images with less noise for a given number of samples.