Display Omitted We present a method to robustly track the geometry of an object that deforms over time.We fit a FEM-based model to the data leading to physically plausible results.We evaluate the performance of our method using synthetic and scanned data. We present an approach to robustly track the geometry of an object that deforms over time from a set of input point clouds captured from a single viewpoint. The deformations we consider are caused by applying forces to known locations on the object's surface. Our method combines the use of prior information on the geometry of the object modeled by a smooth template and the use of a linear finite element method to predict the deformation. This allows the accurate reconstruction of both the observed and the unobserved sides of the object. We present tracking results for noisy low-quality point clouds acquired by either a stereo camera or a depth camera, and simulations with point clouds corrupted by different error terms. We show that our method is also applicable to large non-linear deformations.
Recently, Choi and Szymczak @cite_40 used FEM to predict a consistent set of deformations from a sequence of coarse watertight meshes but their method does not apply to single-view tracking, which is the focus of our method.
@cite_40
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Computing correspondence between time frames of a time-dependent 3D surface is essential for the understanding of its motion and deformation. In particular, it can be a useful tool in compression, editing, texturing, or analysis of the physical or structural properties of deforming objects. However, correspondence information is not trivial to obtain for experimentally acquired 3D animations, such as time-dependent visual hulls (typically represented as either a binary occupancy grid or as a sequence of meshes of varying connectivity). In this article we present a new nonrigid fitting method that can compute such correspondence information for objects that do not undergo large volume or topological changes, such as living creatures. Experimental results show that it is robust enough to handle visual hull data, allowing to convert it into a constant connectivity mesh with vertices moving in time. Our procedure first creates a rest-state mesh from one of the input frames. This rest-state mesh is then fitted to the consecutive frames. We do this by iteratively displacing its vertices so that a combination of surface distance and elastic potential energy is minimized. A novel rotation compensation method enables us to obtain high-quality results with linear elasticity, even in presence of significant bending.