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• W3048442086 abstract "In this paper 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." @default.
• W3048442086 created "2020-08-18" @default.
• W3048442086 creator A5075924127 @default.
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• W3048442086 date "2020-08-12" @default.
• W3048442086 modified "2023-09-25" @default.
• W3048442086 title "ENIGMA" @default.
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• W3048442086 doi "https://doi.org/10.1145/3386569.3392447" @default.
• W3048442086 hasPublicationYear "2020" @default.
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