标题:Computing Smooth Quasi-geodesic Distance Field (QGDF) with Quadratic Programming
作者:Cao, Luming; Zhao, Junhao; Xu, Jian; Chen, Shuangmin; Liu, Guozhu; Xin, Shiqing; Zhou, Yuanfeng; He, Ying
通讯作者:Xin, Shiqing;Xin, Shiqing
作者机构:[Cao, Luming; Zhao, Junhao; Xin, Shiqing] Shandong Univ, Sch Comp Sci & Technol, Jinan, Peoples R China.; [Xu, Jian] Dalian Univ Technol, Sch Chem E 更多
会议名称:Symposium on Solid and Physical Modeling (SPM) collocated with the Shape Modeling International Conference (SMI)
会议日期:JUN 02-04, 2020
来源:COMPUTER-AIDED DESIGN
出版年:2020
卷:127
DOI:10.1016/j.cad.2020.102879
关键词:Smooth geodesic distance field; Quadratic programming; Convex; optimization; Defect-tolerant distances
摘要:Computing geodesic distances on polyhedral surfaces is an important task in digital geometry processing. Speed and accuracy are two commonly-used measurements of evaluating a discrete geodesic algorithm. In applications, such as parametrization and shape analysis, a smooth distance field is often preferred over the exact, non-smooth geodesic distance field. We use the term Quasi-geodesic Distance Field (QGDF) to denote a smooth scalar field that is as close as possible to an exact geodesic distance field. In this paper, we formulate the problem of computing QGDF into a standard quadratic programming (QP) problem which maintains a trade-off between accuracy and smoothness. The proposed QP formulation is also flexible in that it can be naturally extended to point clouds and tetrahedral meshes, and support various user-specified constraints. We demonstrate the effectiveness of QGDF in defect-tolerant distances and symmetry-constrained distances. (C) 2020 Elsevier Ltd. All rights reserved.
收录类别:CPCI-S;EI;SCOPUS;SCIE
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085245124&doi=10.1016%2fj.cad.2020.102879&partnerID=40&md5=fd1cf038113c0338c222a2683934d3b9
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