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Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks
Our experiments demonstrate that constraining the synchronization on the Riemannian manifold $SO(n)$ improves the estimation of the functional maps, while our RLFM sampler provides for the first time an uncertainty quantification of the results.
Topo Sampler: A Topology Constrained Noise Sampling for GANs
This work studies disconnected manifold learning in generative models in the light of point-set topology and persistent homology.
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