Feature-Aware Reconstruction of Volume Data via Trivariate Splines

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Date
2011
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
In 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.
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@inproceedings{
:10.2312/PE/PG/PG2011short/049-054
, booktitle = {
Pacific Graphics Short Papers
}, editor = {
Bing-Yu Chen and Jan Kautz and Tong-Yee Lee and Ming C. Lin
}, title = {{
Feature-Aware Reconstruction of Volume Data via Trivariate Splines
}}, author = {
Li, Bo
and
Qin, Hong
}, year = {
2011
}, publisher = {
The Eurographics Association
}, ISBN = {
978-3-905673-84-5
}, DOI = {
/10.2312/PE/PG/PG2011short/049-054
} }
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