5 papers
Marginal Flow: a flexible and efficient framework for density estimation
Marcello Massimo Negri, Jonathan Aellen, Manuel Jahn +2
Current density modeling approaches suffer from at least one of the following shortcomings: expensive training, slow inference, approximate likelihood, mode collapse or architectur…
Posterior shape models revisited: Improving 3D reconstructions from partial data using target specific models
Jonathan Aellen, Florian Burkhardt, Thomas Vetter +1
In medical imaging, point distribution models are often used to reconstruct and complete partial shapes using a statistical model of the full shape. A commonly overlooked, but cruc…
Injective flows for star-like manifolds
Marcello Massimo Negri, Jonathan Aellen, Volker Roth
Normalizing Flows (NFs) are powerful and efficient models for density estimation. When modeling densities on manifolds, NFs can be generalized to injective flows but the Jacobian d…
Lagrangian Flow Networks for Conservation Laws
F. Arend Torres, Marcello Massimo Negri, Marco Inversi +2
We introduce Lagrangian Flow Networks (LFlows) for modeling fluid densities and velocities continuously in space and time. By construction, the proposed LFlows satisfy the continui…
GiNGR: Generalized Iterative Non-Rigid Point Cloud and Surface Registration Using Gaussian Process Regression
Dennis Madsen, Jonathan Aellen, Andreas Morel-Forster +2
In this paper, we unify popular non-rigid registration methods for point sets and surfaces under our general framework, GiNGR. GiNGR builds upon Gaussian Process Morphable Models (…