Literature DB >> 22442125

Restricted trivariate polycube splines for volumetric data modeling.

Kexiang Wang1, Xin Li, Bo Li, Huanhuan Xu, Hong Qin.   

Abstract

This paper presents a volumetric modeling framework to construct a novel spline scheme called restricted trivariate polycube splines (RTP-splines). The RTP-spline aims to generalize both trivariate T-splines and tensor-product B-splines; it uses solid polycube structure as underlying parametric domains and strictly bounds blending functions within such domains. We construct volumetric RTP-splines in a top-down fashion in four steps: 1) Extending the polycube domain to its bounding volume via space filling; 2) building the B-spline volume over the extended domain with restricted boundaries; 3) inserting duplicate knots by adding anchor points and performing local refinement; and 4) removing exterior cells and anchors. Besides local refinement inherited from general T-splines, the RTP-splines have a few attractive properties as follows: 1) They naturally model solid objects with complicated topologies/bifurcations using a one-piece continuous representation without domain trimming/patching/merging. 2) They have guaranteed semistandardness so that the functions and derivatives evaluation is very efficient. 3) Their restricted support regions of blending functions prevent control points from influencing other nearby domain regions that stay opposite to the immediate boundaries. These features are highly desirable for certain applications such as isogeometric analysis. We conduct extensive experiments on converting complicated solid models into RTP-splines, and demonstrate the proposed spline to be a powerful and promising tool for volumetric modeling and other scientific/engineering applications where data sets with multiattributes are prevalent.

Entities:  

Year:  2012        PMID: 22442125     DOI: 10.1109/TVCG.2011.102

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  3 in total

1.  Impact of Early and Late Visual Deprivation on the Structure of the Corpus Callosum: A Study Combining Thickness Profile with Surface Tensor-Based Morphometry.

Authors:  Natasha Leporé; Yalin Wang; Jie Shi; Olivier Collignon; Liang Xu; Gang Wang; Yue Kang; Franco Leporé; Yi Lao; Anand A Joshi
Journal:  Neuroinformatics       Date:  2015-07

2.  A novel cortical thickness estimation method based on volumetric Laplace-Beltrami operator and heat kernel.

Authors:  Gang Wang; Xiaofeng Zhang; Qingtang Su; Jie Shi; Richard J Caselli; Yalin Wang
Journal:  Med Image Anal       Date:  2015-02-03       Impact factor: 8.545

3.  Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures.

Authors:  Gang Wang; Yalin Wang
Journal:  Neuroimage       Date:  2016-12-26       Impact factor: 6.556

  3 in total

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