M.Sc Student | Edelstein Michal |
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Subject | A Genetic Algorithm for Fully Automatic Non-Isometric Shape Matching |
Department | Department of Electrical Engineering | Supervisor | Professor Mirela Ben-Chen |
Full Thesis text | ![]() |
Shape correspondence is a challenging task in geometry processing and computer graphics with various applications including texture transfer, shape interpolation and statistical modeling. The goal is to find a meaningful relation between two or more shapes. The correspondence can be either sparse, where a small number of meaningful landmarks are matched, or dense, where a full map between the shapes is retrieved. Finding such a map between non-isometric shapes is difficult since the shapes can differ greatly from one another and the matching needs to be semantic.
We propose a fully automatic method for shape correspondence that is widely applicable, and especially effective for non-isometric shapes and shapes of different topology. We observe that fully automatic shape correspondence can be decomposed as a hybrid discrete-continuous optimization problem, and we find the best sparse landmark correspondence, whose sparse-to-dense extension minimizes a local metric distortion. To tackle the combinatorial task of landmark correspondence we use an evolutionary genetic algorithm, where the local distortion of the sparse-to-dense extension is used as the objective function.
We design novel geometrically guided genetic operators, which, when combined with our objective, are highly effective for non-isometric shape matching. Our method outperforms state of the art methods for automatic shape correspondence both quantitatively and qualitatively on challenging datasets.