Feature-Aware Reconstruction of Volume Data via Trivariate Splines

Li, Bo; Qin, Hong

Feature-Aware Reconstruction of Volume Data via Trivariate Splines

Files

049-054.pdf (807 KB)

Date

2011

Authors

Li, Bo
Qin, Hong

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.

        @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
}
}