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CLC number: TP391.7

On-line Access: 2014-09-06

Received: 2014-03-18

Revision Accepted: 2014-07-26

Crosschecked: 2014-08-19

Cited: 1

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.9 P.744-753


Visual salience guided feature-aware shape simplification

Author(s):  Yong-wei Miao, Fei-xia Hu, Min-yan Chen, Zhen Liu, Hua-hao Shou

Affiliation(s):  College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; more

Corresponding email(s):   ywmiao@zjut.edu.cn

Key Words:  Visual salience, Shape simplification, Content-aware, Weighted quadric error metric, Feature-aware

Yong-wei Miao, Fei-xia Hu, Min-yan Chen, Zhen Liu, Hua-hao Shou. Visual salience guided feature-aware shape simplification[J]. Journal of Zhejiang University Science C, 2014, 15(9): 744-753.

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T1 - Visual salience guided feature-aware shape simplification
A1 - Yong-wei Miao
A1 - Fei-xia Hu
A1 - Min-yan Chen
A1 - Zhen Liu
A1 - Hua-hao Shou
J0 - Journal of Zhejiang University Science C
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1400097

In the area of 3D digital engineering and 3D digital geometry processing, shape simplification is an important task to reduce their requirement of large memory and high time complexity. By incorporating the content-aware visual salience measure of a polygonal mesh into simplification operation, a novel feature-aware shape simplification approach is presented in this paper. Owing to the robust extraction of relief heights on 3D highly detailed meshes, our visual salience measure is defined by a center-surround operator on Gaussian-weighted relief heights in a scale-dependent manner. Guided by our visual salience map, the feature-aware shape simplification algorithm can be performed by weighting the high-dimensional feature space quadric error metric of vertex pair contractions with the weight map derived from our visual salience map. The weighted quadric error metric is calculated in a six-dimensional feature space by combining the position and normal information of mesh vertices. Experimental results demonstrate that our visual salience guided shape simplification scheme can adaptively and effectively re-sample the underlying models in a feature-aware manner, which can account for the visually salient features of the complex shapes and thus yield better visual fidelity.



Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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