S. Weiss1, R.Maier2, D. Cremers2, R. Westermann1, N. Thuerey1
1Computer Graphics & Visualization Group, Technische Universität München, Germany
2Computer Vision Group, Technische Universität München, Germany
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.
Correspondence-Free Material Reconstruction using Sparse Surface Constraints
S. Weiss, R. Maier, R. Westermann, D. Cremers, N. Thuerey
(published at CVPR 2020)