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We propose a discrete regularization framework on weighted graphs of arbitrary topology, which unifies local and nonlocal processing of images, meshes, and more generally discrete data. The approach considers the problem as a variational one, which consists in minimizing a weighted sum of two energy terms: a regularization one that uses the discrete(More)
We propose a discrete regularization framework on weighted graphs of arbitrary topology, which unifies image and mesh filtering. The approach considers the problem as a variational one, which consists in minimizing a weighted sum of two energy terms: a regularization one that uses the discrete p-Laplace operator, and an approximation one. This formulation(More)
We describe a system for taking a 2D sketch of a mirror-symmetric 3D shape and lifting the curves to 3D, inferring the symmetry relationship from the original hand-drawn curves. The system takes as input a hand-drawn sketch and generates a set of 3D curves such that their orthogonal projection matches the input sketch. The main contribution is a method(More)
A segmentation and model-reconstruction algorithm is proposed based on polynomial approximation and on a novel version of "region growing". First, an initial partition is calculated on the basis of differential-geometric properties of the range image. Then, the first merging procedure is applied ("merge with constraints") aiming at correctly identifying(More)