Sparse Geometric Representation Through Local Shape Probing

Abstract : We propose a new shape analysis approach based on the non-local analysis of local shape variations. Our method relies on a novel description of shape variations, called Local Probing Field (LPF), which describes how a local probing operator transforms a pattern onto the shape. By carefully optimizing the position and orientation of each descriptor, we are able to capture shape similarities and gather them into a geometrically relevant dictionary over which the shape decomposes sparsely. This new representation permits to handle shapes with mixed intrinsic dimensionality (e.g. shapes containing both surfaces and curves) and to encode various shape features such as boundaries. Our shape representation has several potential applications; here we demonstrate its efficiency for shape resampling and point set denoising for both synthetic and real data. Index Terms—Shape similarity-Local shape descriptor-Point set denoising and resampling.
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IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2017, 〈http://ieeexplore.ieee.org/document/7956272/〉. 〈10.1109/TVCG.2017.2719024〉
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Julie Digne, Sébastien Valette, Raphaëlle Chaine. Sparse Geometric Representation Through Local Shape Probing. IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2017, 〈http://ieeexplore.ieee.org/document/7956272/〉. 〈10.1109/TVCG.2017.2719024〉. 〈hal-01547820〉

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