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Geodesic Remeshing Using Front Propagation
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated colour grading using colour distribution transfer
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A survey of content based 3D shape retrieval methods
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Geodesic Methods in Computer Vision and Graphics
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Shape description by bending invariant moments
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Transportation Distances on the Circle
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Wasserstein barycenter and its application to texture mixing
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Schrödinger diffusion for shape analysis with texture
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Journal of Mathematical Imaging and Vision
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This paper presents a new method for 2-D and 3-D shape retrieval based on geodesic signatures. These signatures are high dimensional statistical distributions computed by extracting several features from the set of geodesic distance maps to each point. The resulting high dimensional distributions are matched to perform retrieval using a fast approximate Wasserstein metric. This allows to propose a unifying framework for the compact description of planar shapes and 3-D surfaces.