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Found 6 papers, 3 papers with code
Bi-invariant Geodesic Regression with Data from the Osteoarthritis Initiative
Many phenomena are naturally characterized by measuring continuous transformations such as shape changes in medicine or articulated systems in robotics.
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.
Predicting cognitive scores with graph neural networks through sample selection learning
On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task.
