Table-driven Adaptive Importance Sampling

Abstract

Monte Carlo rendering algorithms generally rely on some form of importance sampling to evaluate the measurement equation. Most of these importance sampling methods only take local information into account, however, so the actual importance function used may not closely resemble the light distribution in the scene. In this paper, we present Table-driven Adaptive Importance Sampling (TAIS), a sampling technique that augments existing importance functions with tabular importance maps that direct sampling towards undersampled regions of path space. The importance maps are constructed lazily, relying on information gathered during the course of sampling. During sampling the importance maps act either in parallel with or as a preprocess to existing importance sampling methods. We show that our adaptive importance maps can be effective at reducing variance in a number of rendering situations.