Remeshing-assisted Optimization for Locally Injective Mappings

Constructing locally injective mappings for 2D triangular meshes is vital in applications such as deformations. In such a highly constrained optimization, the prescribed tessellation may impose strong restriction on the solution. As a consequence, the feasible region may be too small to contain an i...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computer graphics forum Jg. 33; H. 5; S. 269 - 279
Hauptverfasser: Jin, Y., Huang, J., Tong, R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.08.2014
Schlagworte:
Analysis
and systems
Categories and Subject Descriptors (according to ACM CCS)
Computer graphics
Constrictions
Convergence
Deformation
Graph theory
I.3.5 [Computer Graphics] Computational Geometry and Object Modeling-Geometric algorithms
I.3.5 [Computer Graphics] Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems
languages
Mapping
Mathematical models
Optimization
Studies
Tessellation
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Constructing locally injective mappings for 2D triangular meshes is vital in applications such as deformations. In such a highly constrained optimization, the prescribed tessellation may impose strong restriction on the solution. As a consequence, the feasible region may be too small to contain an ideal solution, which leads to problems of slow convergence, poor solution, or even that no solution can be found. We propose to integrate adaptive remeshing into interior point method to solve this issue. We update the vertex positions via a parameter‐free relaxation enhanced geometry optimization, and then use edge‐flip operations to reduce the residual and keep a reasonable condition number for better convergence. For more robustness, when the iteration of interior point method terminates but leaves the positional constraints unsatisfied, we estimate the edges in the current tessellation that block vertices moving based on the convergence information of the optimization, and then split neighboring edges to break the restriction. The results show that our method has better performance than the solely geometric optimization approaches, especially for extreme deformations.
Bibliographie:istex:D935B344D63509698B4364FA64E1858B4C8C3E6F
Supporting Information
ark:/67375/WNG-MZPGXBVM-4
ArticleID:CGF12452
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12452