Stratified Sampling for Stochastic Transparency

Abstract

The traditional method of rendering semi-transparent surfaces using alpha blending requires sorting the surfaces in depth order. There are several techniques for order-independent transparency, but most require either unbounded storage or can be fragile due to forced compaction of information during rendering. Stochastic transparency works in a fixed amount of storage and produces results with the correct expected value. However, carelessly chosen sampling strategies easily result in high variance of the final pixel colors, showing as noise in the image. In this paper, we describe a series of improvements to stochastic transparency that enable stratified sampling in both spatial and alpha domains. As a result, the amount of noise in the image is significantly reduced, while the result remains unbiased.