Microfacet Model Regularization for Robust Light Transport

Abstract

Today, Monte Carlo light transport algorithms are used in many applications to render realistic images. Depending on the complexity of the used methods, several light effects can or cannot be found by the sampling process. Especially, specular and smooth glossy surfaces often lead to high noise and missing light effects. Path space regularization provides a solution, improving any sampling algorithm, by modifying the material evaluation code. Previously, Kaplanyan and Dachsbacher [KD13] introduced the concept for pure specular interactions. We extend this idea to the commonly used microfacet models by manipulating the roughness parameter prior to the evaluation. We also show that this kind of regularization requires a change in the MIS weight computation and provide the solution. Finally, we propose two heuristics to adaptively reduce the introduced bias. Using our method, many complex light effects are reproduced and the fidelity of smooth objects is increased. Additionally, if a path was sampleable before, the variance is partially reduced.