Manifold Next Event Estimation

Abstract

We present manifold next event estimation (MNEE), a specialised technique for Monte Carlo light transport simulation to render refractive caustics by connecting surfaces to light sources (next event estimation) across transmissive interfaces. We employ correlated sampling by means of a perturbation strategy to explore all half vectors in the case of rough transmission while remaining outside of the context of Markov chain Monte Carlo, improving temporal stability. MNEE builds on differential geometry and manifold walks. It is very lightweight in its memory requirements, as it does not use light caching methods such as photon maps or importance sampling records. The method integrates seamlessly with existing Monte Carlo estimators via multiple importance sampling.