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Global Illumination using Photon Ray Splatting

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44618

Herzog,  Robert
Computer Graphics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44596

Havran,  Vlastimil
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44779

Kinuwaki,  Shinichi
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45095

Myszkowski,  Karol
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Fulltext (public)

herzog07report.pdf
(Any fulltext), 5KB

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Citation

Herzog, R., Havran, V., Kinuwaki, S., Myszkowski, K., & Seidel, H.-P.(2007). Global Illumination using Photon Ray Splatting (MPI-I-2007-4-007). Saarbrücken, Germany: Max-Planck-Institut für Informatik.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1F57-6
Abstract
We present a novel framework for efficiently computing the indirect illumination in diffuse and moderately glossy scenes using density estimation techniques. A vast majority of existing global illumination approaches either quickly computes an approximate solution, which may not be adequate for previews, or performs a much more time-consuming computation to obtain high-quality results for the indirect illumination. Our method improves photon density estimation, which is an approximate solution, and leads to significantly better visual quality in particular for complex geometry, while only slightly increasing the computation time. We perform direct splatting of photon rays, which allows us to use simpler search data structures. Our novel lighting computation is derived from basic radiometric theory and requires only small changes to existing photon splatting approaches. Since our density estimation is carried out in ray space rather than on surfaces, as in the commonly used photon mapping algorithm, the results are more robust against geometrically incurred sources of bias. This holds also in combination with final gathering where photon mapping often overestimates the illumination near concave geometric features. In addition, we show that our splatting technique can be extended to handle moderately glossy surfaces and can be combined with traditional irradiance caching for sparse sampling and filtering in image space.