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    GPU-Accelerated Deep Shadow Maps for Direct Volume Rendering

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    049-052.pdf (239.8Kb)
    Date
    2006
    Author
    Hadwiger, Markus ORCID
    Kratz, Andrea
    Sigg, Christian
    Bühler, Katja
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    Abstract
    Deep shadow maps unify the computation of volumetric and geometric shadows. For each pixel in the shadow map, a fractional visibility function is sampled, pre-filtered, and compressed as a piecewise linear function. However, the original implementation targets software-based off-line rendering. Similar previous algorithms on GPUs focus on geometric shadows and lose many important benefits of the original concept. We focus on shadows for interactive direct volume rendering, where shadow algorithms currently either compute additional per-voxel shadow data, or employ half-angle slicing to generate shadows during rendering. We adapt the original concept of deep shadow maps to volume ray-casting on GPUs, and show that it can provide anti-aliased high-quality shadows at interactive rates. Ray-casting is used for both generation of the shadow map data structure and actual rendering. High frequencies in the visibility function are captured by a pre-computed lookup table for piecewise linear segments. Direct volume rendering is performed with an additional deep shadow map lookup for each sample. Overall, we achieve interactive high-quality volume ray-casting with accurate shadows. To conclude, we briefly describe how semi-transparent geometry such as hair could be integrated as well, provided that rasterization can write to arbitrary locations in a texture. This would be a major step toward full deep shadow map functionality.
    BibTeX
    @inproceedings {10.2312:EGGH:EGGH06:049-052,
    booktitle = {Graphics Hardware},
    editor = {Marc Olano and Philipp Slusallek},
    title = {{GPU-Accelerated Deep Shadow Maps for Direct Volume Rendering}},
    author = {Hadwiger, Markus and Kratz, Andrea and Sigg, Christian and Bühler, Katja},
    year = {2006},
    publisher = {The Eurographics Association},
    ISSN = {1727-3471},
    ISBN = {3-905673-37-1},
    DOI = {10.2312/EGGH/EGGH06/049-052}
    }
    URI
    http://dx.doi.org/10.2312/EGGH/EGGH06/049-052
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