6 citations · 14 across the 7 of their papers we have counts for
7 papers
Geodesic Calculus on Implicitly Defined Latent Manifolds
Florine Hartwig, Josua Sassen, Juliane Braunsmann +2
Latent manifolds of autoencoders provide low-dimensional representations of data, which can be studied from a geometric perspective. We propose to describe these latent manifolds a…
SDFs from Unoriented Point Clouds using Neural Variational Heat Distances
Samuel Weidemaier, Florine Hartwig, Josua Sassen +3
We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from unoriented point clouds. To this end, we replace the commonly used eikonal equation w…
A Review on Riemannian Metric Learning: Closer to You than You Imagine
Samuel Gruffaz, Josua Sassen
Riemannian metric learning is an emerging field in machine learning, unlocking new ways to encode complex data structures beyond traditional distance metric learning. While classic…
A hybrid minimizing movement and neural network approach to Willmore flow
Martin Rumpf, Josua Sassen, Christoph Smoch
We present a hybrid method combining a minimizing movement scheme with neural operators for the simulation of phase field-based Willmore flow. The minimizing movement component is…
Parametrizing Product Shape Manifolds by Composite Networks
Josua Sassen, Klaus Hildebrandt, Martin Rumpf +1
Parametrizations of data manifolds in shape spaces can be computed using the rich toolbox of Riemannian geometry. This, however, often comes with high computational costs, which ra…
A Pessimistic Bilevel Stochastic Problem for Elastic Shape Optimization
Johanna Burtscheidt, Matthias Claus, Sergio Conti +3
We consider pessimistic bilevel stochastic programs in which the follower maximizes over a fixed compact convex set a strictly convex quadratic function, whose Hessian depends on t…
Geometric optimization using nonlinear rotation-invariant coordinates
Josua Sassen, Behrend Heeren, Klaus Hildebrandt +1
Geometric optimization problems are at the core of many applications in geometry processing. The choice of a representation fitting an optimization problem can considerably simplif…