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.
@cite_28 avoid the use of a template model by initializing the tracking procedure with the visual hull of the object. @cite_42 track a deformable model using a skeleton-based approach, where the skeleton is computed using the data.
@cite_28 @cite_42
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We present a novel shape completion technique for creating temporally coherent watertight surfaces from real-time captured dynamic performances. Because of occlusions and low surface albedo, scanned mesh sequences typically exhibit large holes that persist over extended periods of time. Most conventional dynamic shape reconstruction techniques rely on template models or assume slow deformations in the input data. Our framework sidesteps these requirements and directly initializes shape completion with topology derived from the visual hull. To seal the holes with patches that are consistent with the subject's motion, we first minimize surface bending energies in each frame to ensure smooth transitions across hole boundaries. Temporally coherent dynamics of surface patches are obtained by unwarping all frames within a time window using accurate interframe correspondences. Aggregated surface samples are then filtered with a temporal visibility kernel that maximizes the use of nonoccluded surfaces. A key benefit of our shape completion strategy is that it does not rely on long-range correspondences or a template model. Consequently, our method does not suffer error accumulation typically introduced by noise, large deformations, and drastic topological changes. We illustrate the effectiveness of our method on several high-resolution scans of human performances captured with a state-of-the-art multiview 3D acquisition system. We introduce the notion of consensus skeletons for non-rigid space-time registration of a deforming shape. Instead of basing the registration on point features, which are local and sensitive to noise, we adopt the curve skeleton of the shape as a global and descriptive feature for the task. Our method uses no template and only assumes that the skelet al structure of the captured shape remains largely consistent over time. Such an assumption is generally weaker than those relying on large overlap of point features between successive frames, allowing for more sparse acquisition across time. Building our registration framework on top of the low-dimensional skeletontime structure avoids heavy processing of dense point or volumetric data, while skeleton consensusization provides robust handling of incompatibilities between per-frame skeletons. To register point clouds from all frames, we deform them by their skeletons, mirroring the skeleton registration process, to jump-start a non-rigid ICP. We present results for non-rigid space-time registration under sparse and noisy spatio-temporal sampling, including cases where data was captured from only a single view.