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December 23, Wednesday
11:00 – 12:00

Most 3D shapes are nowadays acquired using range scanning devices.Currently, scanners are capable of capturing complex shapes, large urban scenes and lately even motion. The initial representation of the shape consists of several properly transformed depth images, resulting in a point sampling of the surface. Typically, scan data consist of missing parts, noise in point coordinates and orientation, outliers and non-uniform sampled regions. Without prior assumptions and user interventions, the reconstruction problem is ill posed; an infinite number of surfaces pass through or near the data points. One of today's principal challenges is the development of robust point processing and reconstruction techniques that deal with the inherent inconsistencies in the acquired data set. In my talk I will present recent advances in geometric modeling, processing and reconstruction of point data. I will describe a deformable model for watertight manifold reconstruction. The model yields a correct topology interpretation of the reconstructed shape and allows topology control to a certain extent. Next, I will present a topology-aware interactive reconstruction technique. Topological ambiguities in the data are automatically detected and user interaction is used to consolidate topology reconstruction. Following, I will present a space-time technique for the reconstruction of moving and deforming objects. The motion of the object is described as an incompressible flow of matter which overcomes deficiencies in the acquired data such as persistent occlusions, errors and even entirely missing frames. Motivated by recent advancements in sparse signal reconstruction, I will present a "lower-than-L2" minimization scheme for sparse reconstruction. The sparsity principle gives rise to a novel global reconstruction paradigm for sharp point set surfaces which is robust to noise.