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Konferenzbeitrag

Lighting Details Preserving Photon Density Estimation

MPG-Autoren
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/persons45449

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

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Zitation

Herzog, R., & Seidel, H.-P. (2007). Lighting Details Preserving Photon Density Estimation. In M. Alexa, S. Gortler, & T. Ju (Eds.), Pacific Graphics 2007: The [15th] Pacific Conference on Computer Graphics and Applications (pp. 407-410). Los Alamitos, CA, USa: IEEE Computer Society.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1FB3-3
Zusammenfassung
Standard density estimation approaches suffer from visible bias due to low-pass filtering of the lighting function. Therefore, most photon density estimation methods have been used primarily with inefficient Monte Carlo final gathering to achieve high-quality results for the indirect illumination. We present a density estimation technique for efficiently computing all-frequency global illumination in diffuse and moderately glossy scenes. In particular, we compute the direct, indirect, and caustics illumination during photon tracing from the light sources. Since the high frequencies in the illumination often arise from visibility changes and surface normal variations, we consider a kernel that takes these factors into account. To efficiently detect visibility changes, we introduce a hierarchical voxel data structure of the scene geometry, which is generated on GPU. Further, we preserve the surface orientation by computing the density estimation in ray space.