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Found 7 papers, 2 papers with code
Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs
We propose two graph neural network layers for graphs with features in a Riemannian manifold.
Intrinsic shape analysis in archaeology: A case study on ancient sundials
By performing regression in shape space, we find that for Roman sundials, the bend of the sundials' shadow-receiving surface changes with the location's latitude.
Sasaki Metric for Spline Models of Manifold-Valued Trajectories
We propose a generic spatiotemporal framework to analyze manifold-valued measurements, which allows for employing an intrinsic and computationally efficient Riemannian hierarchical model.
Predicting Shape Development: a Riemannian Method
Predicting the future development of an anatomical shape from a single baseline observation is a challenging task.
A Kendall Shape Space Approach to 3D Shape Estimation from 2D Landmarks
Here, we present a new approach based on Kendall's shape space to reconstruct 3D shapes from single monocular 2D images.
Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms
shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data.
Geodesic B-Score for Improved Assessment of Knee Osteoarthritis
Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status.
