32 citations · 32 across the 2 of their papers we have counts for
6 papers
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go
Marvin Eisenberger, David Novotny, Gael Kerchenbaum +4
We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i.e. in a single feed forward pass, a smooth interpolation and po…
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +1
We propose a novel unsupervised learning approach to 3D shape correspondence that builds a multiscale matching pipeline into a deep neural network. This approach is based on smooth…
Hamiltonian Dynamics for Real-World Shape Interpolation
Marvin Eisenberger, Daniel Cremers
We revisit the classical problem of 3D shape interpolation and propose a novel, physically plausible approach based on Hamiltonian dynamics. While most prior work focuses on synthe…
Smooth Shells: Multi-Scale Shape Registration with Functional Maps
Marvin Eisenberger, Zorah Lähner, Daniel Cremers
We propose a novel 3D shape correspondence method based on the iterative alignment of so-called smooth shells. Smooth shells define a series of coarse-to-fine shape approximations…
Divergence-Free Shape Interpolation and Correspondence
Marvin Eisenberger, Zorah Lähner, Daniel Cremers
We present a novel method to model and calculate deformation fields between shapes embedded in . Our framework combines naturally interpolating the two input shapes a…
Fast sampling of parameterised Gaussian random fields
Jonas Latz, Marvin Eisenberger, Elisabeth Ullmann
Gaussian random fields are popular models for spatially varying uncertainties, arising for instance in geotechnical engineering, hydrology or image processing. A Gaussian random fi…