Path differentials and applications

Abstract

Photo-realistic rendering algorithms such as Monte Carlo ray tracing sample individual paths to compute images. Noise and aliasing artefacts are usually reduced by supersampling. Knowledge about the neighborhood of the path, such as an estimated footprint, can be used to reduce these artefacts without having to trace additional paths. The recently introduced ray differentials estimate such a footprint for classical ray tracing, by computing ray derivatives with respect to the image plane. The footprint proves to be useful for filtering textures locally on surfaces. In this paper, we generalize the use of these derivatives to arbitrary path sampling, including general reflection and refraction functions. Sampling new directions introduces additional partial derivatives, which are all combined into a footprint estimate. Additionally the path gradient is introduced; it gives the rate of change of the path contribution. When this change is too steep the size of the footprint is reduced. The resulting footprint can be used in any global illumination algorithm that is based on path sampling. Two applications show its potential: texture filtering in distributed ray tracing and a novel hierarchical approach to particle tracing radiosity.