Rabeeh Karimi Mahabadi

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Dense 3D reconstruction still remains a hard task for a broad number of object classes which are not sufficiently textured or contain transparent and reflective parts. Shape priors are the tool of choice when the input data itself is not descriptive enough to get a faithful reconstruction. We propose a novel shape prior formulation that splits the object(More)
We consider the class of convex minimization problems, composed of a self-concordant function, such as the log det metric, a convex data fidelity term h(·) and, a regularizing – possibly non-smooth – function g(·). This type of problems have recently attracted a great deal of interest, mainly due to their omnipresence in top-notch applications. Under this(More)
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