Measurement-Based Model Estimation for Deformable Objects
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.