Measurement-Based Model Estimation for Deformable Objects
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2014-09
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Universidad Rey Juan Carlos
Resumen
Deformable objects play a critical role in our life due to their compliance. Clothing and
support structures, such as mattresses, are just a few examples of their use. They are so
common that an accurate prediction of their behavior under a variety of environments and
situations is mandatory in order to design products with the desired functionalities.
However, obtaining realistic simulations is a difficult task. Both, an appropriate deformation
model and parameters that produce the desired behavior must be used. On one hand,
there exist many deformation models for elasticity, but there are few capable of capturing
other complex effects that are critical in order to obtain the desired realism. On the other
hand, the task of estimating model parameters is usually performed using a trial-and-error
method, with the corresponding waste in time.
In this thesis we develop novel deformation models and parameter estimation methods
that allow us to increase the realism of deformable object simulations. We present deformation
models that capture several of these complex effects: hyperelasticity, extreme nonlinearities,
heterogeneities and internal friction. In addition, we design parameter estimation
methods that take advante of the structure of the measured data and avoid common problems
that arise when numerial optimization algorithms are used.
First, we focus on cloth and present a novel measurement system that captures the behavior
of cloth under a variety of experiments. It produces a complete set of information
including the 3D reconstruction of the cloth sample under test as well as the forces being
applied. We design a parameter estimation pipeline and use this system to estimate parameters
for several popular cloth models and evaluate their performance and suitability in terms
of quality of the obtained estimations.
We then develop a novel, general and flexible deformation model based on additive
energy density terms. By using independent components this model allows us to isolate
the effect that each one has on the global behavior of the deformable object, replicate existing
deformation models and produce new ones. It also allows us to apply incremental
approaches to parameter estimation. We demonstrate its advantages by applying it in a wide
variety of scenarios, including cloth simulation, modeling of heterogeneous soft tissue and capture of extreme nonlinearities in finger skin.
Finally, a fundamental observation extracted from the estimation of parameters for cloth
models is that, in real-world, cloth hysteresis has a huge effect in the mechanical behavior
and visual appearance of cloth. The source of hysteresis is the internal friction produced by
the interactions between yarns. Mechanically, it can produce very different deformations in
the loading or unloading cycles, while visually, it is responsible for effects such as persistent
deformations, preferred wrinkles or history-dependent folds. We develop an internal friction
model, present a measurement and estimation system that produces elasticity and internal
friction parameters, and analyse the visual impact of internal friction in cloth simulation.
Descripción
Tesis Doctoral leída en la Universidad Rey Juan Carlos de Madrid en 2014. Director de la Tesis: Dr. D. Miguel A. Otaduy Tristán