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dc.contributor.authorXu, Fangen_US
dc.contributor.authorMueller, Klausen_US
dc.contributor.editorRaghu Machiraju and Torsten Moelleren_US
dc.date.accessioned2014-01-29T17:49:57Z
dc.date.available2014-01-29T17:49:57Z
dc.date.issued2006en_US
dc.identifier.isbn3-905673-41-Xen_US
dc.identifier.issn1727-8376en_US
dc.identifier.urihttp://dx.doi.org/10.2312/VG/VG06/023-030en_US
dc.description.abstractTraditional volume rendering approaches rely on obtaining values of sampled points in volumetric space, typically on a cartesian grid. Often, this cartesian grid is not the original source of the data. For example, in tomographic imaging applications, such as used in diagnostic medical or industrial CT, the primary source of the data is the set of X-ray projections taken around the object. To enable visualization with established volume rendering methods, the volume must first be reconstructed from these projections. Since sampling is involved, this process introduces errors, adversely impacting image quality. Recently a new rendering technique was proposed, named D2VR, which skips the intermediate reconstruction step entirely and samples the projections directly. It was shown that doing so can improve image quality significantly. But despite its great promise, a shortcoming of the method was its comparatively slow rendering speed. Interactive or at least near-interactive speed, however, is critical for clinical deployment of a visualization framework. To address this shortcoming, our paper proposes a GPU-accelerated D2VR, with facilities for occlusion culling and empty space skipping to achieve further speedups.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CSS): I.3.3 [Computer Graphics]: Display Algorithms.en_US
dc.titleGPU-Accelerated D2VRen_US
dc.description.seriesinformationVolume Graphicsen_US


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