Path Space Regularization for Holistic and Robust Light Transport

Abstract

We propose a simple yet powerful regularization framework for robust light transport simulation. It builds on top of existing unbiased methods and resorts to a consistent estimation using regularization only for paths which cannot be sampled in an unbiased way. To introduce as little bias as possible, we selectively regularize individual interactions along paths, and also derive the regularization consistency conditions. Our approach is compatible with the majority of unbiased methods, e.g. (bidirectional) path tracing and Metropolis light transport (MLT), and only a simple modification is required to adapt existing renderers. We compare to recent unbiased and consistent methods and show examples of scenes with difficult light paths, where regularization is required to account for all illumination features. When coupled with MLT we are able to sample all phenomena, like recent consistent methods, while achieving superior convergence.