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Spectral Shape Recovery and Analysis Via Data-driven Connections
We introduce a novel learning-based method to recover shapes from their Laplacian spectra, based on establishing and exploring connections in a learned latent space. The core of our approach consists in a cycle-consistent module that maps between a learned latent space and sequences of eigenvalues....
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550494/ https://www.ncbi.nlm.nih.gov/pubmed/34720402 http://dx.doi.org/10.1007/s11263-021-01492-6 |