Survey on Discrete Surface Ricci Flow

Ricci flow deforms the Riemannian metric proportionally to the curvature, such that the curvature evolves according to a nonlinear heat diffusion process, and becomes constant eventually. Ricci flow is a powerful computational tool to design Riemannian metrics by prescribed curvatures This work surv...

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Published inJournal of computer science and technology Vol. 30; no. 3; pp. 598 - 613
Main Author 章敏 曾薇 郭任 罗锋 顾险峰
Format Journal Article
LanguageEnglish
Published New York Springer US 01.05.2015
Springer Nature B.V
Department of Computer Science, State University of New York at Stony Brook, Stony Brook, NY 11794-4400, U.S.A.%School of Computing and Information Sciences, Florida International University, Miami, Florida 33199, U.S.A.%Department of Mathematics, 0regon State University, Corvallis, 0R 97331-4605, U.S.A.%Department of Mathematics, Rutgers University, Piscataway, NJ 08854, U.S.A
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Summary:Ricci flow deforms the Riemannian metric proportionally to the curvature, such that the curvature evolves according to a nonlinear heat diffusion process, and becomes constant eventually. Ricci flow is a powerful computational tool to design Riemannian metrics by prescribed curvatures This work surveys the theory of discrete surface Ricci flow registration and shape analysis. Surface Ricci flow has been generalized to the discrete setting. its computational algorithms, and the applications for surface
Bibliography:Ricci flow, discrete, Riemannian metric, Ricci energy, uniformization theory
11-2296/TP
Ricci flow deforms the Riemannian metric proportionally to the curvature, such that the curvature evolves according to a nonlinear heat diffusion process, and becomes constant eventually. Ricci flow is a powerful computational tool to design Riemannian metrics by prescribed curvatures This work surveys the theory of discrete surface Ricci flow registration and shape analysis. Surface Ricci flow has been generalized to the discrete setting. its computational algorithms, and the applications for surface
Min Zhang, Wei Zeng, Ren Guo, Feng Luo, Xianfeng David Gu (1Department of Computer Science, State University of New York at Stony Brook, Stony Brook, NY 11794-4400, U.S.A 2School of Computing and Information Sciences, Florida International University, Miami, Florida 33199, U.S.A. 3Department of Mathematics, Oregon State University, Corvallis, OR 97331-4605, U.S.A. 4Department of MaLhematics, Rutgers University, Piscataway, NJ 08854, U.S.A.)
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-015-1548-8