Search Results for author:
Found 5 papers, 3 papers with code
Basis restricted elastic shape analysis on the space of unregistered surfaces
The use of such bases allows to simplify the representation of the corresponding shape space to a finite dimensional latent space.
VariGrad: A Novel Feature Vector Architecture for Geometric Deep Learning on Unregistered Data
We present a novel geometric deep learning layer that leverages the varifold gradient (VariGrad) to compute feature vector representations of 3D geometric data.
BaRe-ESA: A Riemannian Framework for Unregistered Human Body Shapes
We present Basis Restricted Elastic Shape Analysis (BaRe-ESA), a novel Riemannian framework for human body scan representation, interpolation and extrapolation.
Elastic shape analysis of surfaces with second-order Sobolev metrics: a comprehensive numerical framework
This paper introduces a set of numerical methods for Riemannian shape analysis of 3D surfaces within the setting of invariant (elastic) second-order Sobolev metrics.
Supervised deep learning of elastic SRV distances on the shape space of curves
Motivated by applications from computer vision to bioinformatics, the field of shape analysis deals with problems where one wants to analyze geometric objects, such as curves, while ignoring actions that preserve their shape, such as translations, rotations, or reparametrizations.
