1 research outputs found
Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction
We address the problem to infer physical material parameters and boundary
conditions from the observed motion of a homogeneous deformable object via the
solution of an inverse problem. Parameters are estimated from potentially
unreliable real-world data sources such as sparse observations without
correspondences. We introduce a novel Lagrangian-Eulerian optimization
formulation, including a cost function that penalizes differences to
observations during an optimization run. This formulation matches
correspondence-free, sparse observations from a single-view depth sequence with
a finite element simulation of deformable bodies. In conjunction with an
efficient hexahedral discretization and a stable, implicit formulation of
collisions, our method can be used in demanding situation to recover a variety
of material parameters, ranging from Young's modulus and Poisson ratio to
gravity and stiffness damping, and even external boundaries. In a number of
tests using synthetic datasets and real-world measurements, we analyse the
robustness of our approach and the convergence behavior of the numerical
optimization scheme