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dc.contributor.authorReichl, Florianen_US
dc.contributor.authorChajdas, Matthäus G.en_US
dc.contributor.authorBürger, Kaien_US
dc.contributor.authorWestermann, Rüdigeren_US
dc.contributor.editorMichael Goesele and Thorsten Grosch and Holger Theisel and Klaus Toennies and Bernhard Preimen_US
dc.date.accessioned2013-11-08T10:35:15Z
dc.date.available2013-11-08T10:35:15Z
dc.date.issued2012en_US
dc.identifier.isbn978-3-905673-95-1en_US
dc.identifier.urihttp://dx.doi.org/10.2312/PE/VMV/VMV12/047-054en_US
dc.description.abstractThe performance of rasterization-based rendering on current GPUs strongly depends on the abilities to avoid overdraw and to prevent rendering triangles smaller than the pixel size. Otherwise, the rates at which highresolution polygon models can be displayed are affected significantly. Instead of trying to build these abilities into the rasterization-based rendering pipeline, we propose an alternative rendering pipeline implementation that uses rasterization and ray-casting in every frame simultaneously to determine eye-ray intersections. To make ray-casting competitive with rasterization, we introduce a memory-efficient sample-based data structure which gives rise to an efficient ray traversal procedure. In combination with a regular model subdivision, the most optimal rendering technique can be selected at run-time for each part. For very large triangle meshes our method can outperform pure rasterization and requires a considerably smaller memory budget on the GPU. Since the proposed data structure can be constructed from any renderable surface representation, it can also be used to efficiently render isosurfaces in scalar volume fields. The compactness of the data structure allows rendering from GPU memory when alternative techniques already require exhaustive paging.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.7 [Computer Graphics]en_US
dc.subjectComputer Graphicsen_US
dc.subjectThree Dimensional Graphics and Realismen_US
dc.titleHybrid Sample-based Surface Renderingen_US
dc.description.seriesinformationVision, Modeling and Visualizationen_US


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  • VMV12
    ISBN 978-3-905673-95-1

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