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Modeling and Estimation of Energy‐Based Hyperelastic Objects
Author(s) -
Miguel Eder,
Miraut David,
Otaduy Miguel A.
Publication year - 2016
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12840
Subject(s) - hyperelastic material , computer science , robustness (evolution) , nonlinear system , separable space , energy (signal processing) , ogden , elastic energy , mathematical optimization , algorithm , mathematics , physics , mathematical analysis , biochemistry , chemistry , statistics , quantum mechanics , gene , thermodynamics
In this paper, we present a method to model hyperelasticity that is well suited for representing the nonlinearity of real‐world objects, as well as for estimating it from deformation examples. Previous approaches suffer several limitations, such as lack of integrability of elastic forces, failure to enforce energy convexity, lack of robustness of parameter estimation, or difficulty to model cross‐modal effects. Our method avoids these problems by relying on a general energy‐based definition of elastic properties. The accuracy of the resulting elastic model is maximized by defining an additive model of separable energy terms, which allow progressive parameter estimation. In addition, our method supports efficient modeling of extreme nonlinearities thanks to energy‐limiting constraints. We combine our energy‐based model with an optimization method to estimate model parameters from force‐deformation examples, and we show successful modeling of diverse deformable objects, including cloth, human finger skin, and internal human anatomy in a medical imaging application.