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Mixing Monte Carlo and Progressive Rendering for Improved Global Illumination

Authors: Ian C. Doidge, Mark W. Jones, Benjamin Mora

DOI: 10.1007/s00371-012-0703-2

Abstract:

In this paper we seek to eliminate the noise caused by caustic paths during progressive Monte Carlo path tracing. We employ a ltering strategy over path space, handling each subspace using specialised deriva-tions of path tracing and progressive photon mapping.

Evaluating di use paths with path tracing allows the use of sample strati cation over both pixels and the image as a whole, whilst sharp detailed caustics are produced using progressive photon mapping. This is an ecient, low noise progressive algorithm with vanish-
ing bias combining the advantages of both Monte Carlo methods, and particle tracing.

Link to Paper

Authors

Dr Mark Jones

Dr Mark Jones

Photon mapping, ray tracing, global illumination, visualization, kernel density estimation, accelerometry data, transfer functions, probability density functions, clustering.