Clustered Importance Sampling for Fast Reflectance Rendering

Abstract: In realistic image synthesis, a major challenge is how to account for largescale, complex lighting environments in an ecient and robust way. This thesis presents a simple and ecient method for bidirectional importance sampling from large-scale illumination sources. The method is based on a clustered representation of the potential light source samples. Combined with an ecient BRDF sampling strategy, an importance function can rapidly be constructed and used to select the lighting samples that will contribute the most in the rendering equation. Most existing methods similar to the one presented in this thesis are restricted to environment lighting only; in contrast, the proposed algorithm can also be used in local lighting scenarios where the illumination exists on an unstructured point set, which may not have the same natural parameterization as an environment map.