个人信息Personal Information
副教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:软件学院、国际信息与软件学院
学科:软件工程. 计算机应用技术. 计算数学
办公地点:信息楼317
联系方式:0411-62274427 250066715@qq.com
电子邮箱:sfwang@dlut.edu.cn
Hierarchical feature subspace for structure-preserving deformation
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论文类型:期刊论文
发表时间:2013-02-01
发表刊物:COMPUTER-AIDED DESIGN
收录刊物:SCIE、EI
卷号:45
期号:2
页面范围:545-550
ISSN号:0010-4485
关键字:Structure-preserving deformation; Feature subspace; Energy optimization; Reconstruction
摘要:This paper aims to propose a new framework for structure-preserving deformation, which is interactive, stable, and easy to use. The deformation is characterized by a nonlinear optimization problem that retains features and structures while allowing user-input external forces. The proposed framework consists of four major steps: feature analysis, ghost construction, energy optimization, and reconstruction. We employ a local structure-tensor-based feature analysis to acquire prior knowledge of the features and structures, which can be properly enforced throughout the deformation process. A ghost refers to a hierarchical feature subspace of the shape. It is constructed to control the original shape deformation in a user-transparent fashion, and speed up our algorithm while best accommodating the deformation. A feature-aware reconstruction is devised to rapidly map the deformation in the subspace back to the original space. Our user interaction is natural and friendly; far fewer point constraints and click-and-drag operations are necessary to achieve the flexible shape deformation goal. Various experiments are conducted to demonstrate the ease of manipulation and high performance of our method. (C) 2012 Elsevier Ltd. All rights reserved.