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Found 5 papers, 2 papers with code
Parametrizing Product Shape Manifolds by Composite Networks
Parametrizations of data manifolds in shape spaces can be computed using the rich toolbox of Riemannian geometry.
Convergent autoencoder approximation of low bending and low distortion manifold embeddings
The encoder embeds the input data manifold into a lower-dimensional latent space, while the decoder represents the inverse map, providing a parametrization of the data manifold by the manifold in latent space.
Learning low bending and low distortion manifold embeddings
The encoder provides an embedding from the input data manifold into a latent space which may then be used for further processing.
Shell PCA: Statistical Shape Modelling in Shell Space
In this paper we describe how to perform Principal Components Analysis in "shell space".
Cortical Surface Co-Registration based on MRI Images and Photos
Brain shift, i. e. the change in configuration of the brain after opening the dura mater, is a key problem in neuronavigation.
