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Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis
[article]
2019
arXiv
pre-print
We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision. Building on this, we (i) design a loss function capable of modelling data variance on PCs which are unstructured, and (ii) regularise the latent space as in a variational auto-encoder, both of which increase the auto-encoders' descriptive capacity while making them probabilistic. Evaluating the reconstruction quality of our architectures, we employ them for
arXiv:1907.09254v2
fatcat:pqbjp44zi5bnxciz4luowu5oem