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.
Most objects deform gradually over time, without abrupt changes in geometry or topology, such as changes in genus. Correct space-time reconstruction of such objects should satisfy this gradual change prior. This requirement necessitates a globally consistent interpretation of spatial adjacency. Consider the capture of a surface that comes in contact with itself during the deformation process, such as a hand with different fingers touching one another in parts of the sequence. Naive reconstruction would glue the contact regions together for the duration of each contact and keep them apart in other parts of the sequence. However such reconstruction violates the gradual change prior as it enforces a drastic intrinsic change in the object's geometry at the transition between the glued and unglued sub-sequences. Instead consistent global reconstruction should keep the surfaces separate throughout the entire sequence. We introduce a new method for globally consistent space-time geometry and motion reconstruction from video capture. We use the gradual change prior to resolve inconsistencies and faithfully reconstruct the geometry and motion of the scanned objects. In contrast to most previous methods our algorithm doesn't require a strong shape prior such as a template and provides better results than other template-free approaches. We propose a novel approach to reconstruct complete 3D deformable models over time by a single depth camera, provided that most parts of the models are observed by the camera at least once. The core of this algorithm is based on the assumption that the deformation is continuous and predictable in a short temporal interval. While the camera can only capture part of a whole surface at any time instant, partial surfaces reconstructed from different times are assembled together to form a complete 3D surface for each time instant, even when the shape is under severe deformation. A mesh warping algorithm based on linear mesh deformation is used to align different partial surfaces. A volumetric method is then used to combine partial surfaces, fix missing holes, and smooth alignment errors. Our experiment shows that this approach is able to reconstruct visually plausible 3D surface deformation results with a single camera.