Feature-Aware Reconstruction of Volume Data via Trivariate Splines

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dc.contributor.authorLi, Boen_US
dc.contributor.authorQin, Hongen_US
dc.contributor.editorBing-Yu Chen and Jan Kautz and Tong-Yee Lee and Ming C. Linen_US
dc.date.accessioned2013-10-31T09:37:07Z
dc.date.available2013-10-31T09:37:07Z
dc.date.issued2011en_US
dc.description.abstractIn this paper, we propose a novel approach that transforms discrete volumetric data directly acquired from scanning devices into continuous spline representation with tensor-product regular structure. Our method is achieved through three major steps as follows. First, in order to capture fine features, we construct an as-smooth-as-possible frame field, satisfying a sparse set of directional constraints. Next, a globally smooth parameterization is computed, with iso-parameter curves following the frame field directions. We utilize the parameterization to remesh the data and construct a set of regular-structured volumetric patch layouts, consisting of a small number of volumetric patches while enforcing good feature alignment. Finally, we construct trivariate T-splines on all patches to model geometry and density functions simultaneously. Compared with conventional discrete data, our data-splineconversion results are more efficient and compact, serving as a powerful toolkit with broader application appeal in shape modeling, GPU computing, data reduction, scientific visualization, and physical analysis.en_US
dc.description.seriesinformationPacific Graphics Short Papersen_US
dc.identifier.isbn978-3-905673-84-5en_US
dc.identifier.urihttps://doi.org/10.2312/PE/PG/PG2011short/049-054en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling -Curve, surface, solid, and object representationsen_US
dc.titleFeature-Aware Reconstruction of Volume Data via Trivariate Splinesen_US
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