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Manifold Next Event Estimation
Author(s) -
Hanika Johannes,
Droske Marc,
Fascione Luca
Publication year - 2015
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12681
Subject(s) - monte carlo method , computer science , estimator , markov chain monte carlo , importance sampling , rejection sampling , sampling (signal processing) , manifold (fluid mechanics) , context (archaeology) , algorithm , event (particle physics) , hybrid monte carlo , statistical physics , computer vision , mathematics , physics , statistics , mechanical engineering , paleontology , filter (signal processing) , quantum mechanics , engineering , biology
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.