Generation of Tubular and Membranous Shape Textures with Curvature Functionals

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Abstract

Tubular and membranous shapes display a wide range of morphologies that are difficult to analyze within a common framework. By generalizing the classical Helfrich energy of biomembranes, we model them as solutions to a curvature optimization problem in which the principal curvatures may play asymmetric roles. We then give a novel phase-field formulation to approximate this geometric problem, and study its Gamma-limsup convergence. This results in an efficient GPU algorithm that we validate on well-known minimizers of the Willmore energy; the software for the implementation of our algorithm is freely available online. Exploring the space of parameters reveals that this comprehensive framework leads to a wide continuum of shape textures. This first step towards a unifying theory will have several implications, in biology for quantifying tubular shapes or designing bio-mimetic scaffolds, but also in computer graphics, materials science, or architecture.

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Song, A. (2022). Generation of Tubular and Membranous Shape Textures with Curvature Functionals. Journal of Mathematical Imaging and Vision, 64(1), 17–40. https://doi.org/10.1007/s10851-021-01049-9

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