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Partial Functional Correspondence
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In this paper, we propose a method for computing partial functional correspondence between non-rigid shapes. We use perturbation analysis to show how removal of shape parts changes the Laplace-Beltrami eigenfunctions, and exploit it as a prior on the spectral representation of the correspondence. Corresponding parts are optimization variables in our problem and are used to weight the functional correspondence; we are looking for the largest and most regular (in the Mumford-Shah sense) parts that minimize correspondence distortion. We show that our approach can cope with very challenging correspondence settings.
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Cited by 1 Pith paper
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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence
MDND learns unsupervised shape correspondences by using a non-differentiable hybrid-basis wavelet refiner (HWF) as a fixed target for a differentiable feature network.
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