54 citations · 99 across the 9 of their papers we have counts for
4 papers · 1 filter
Self-supervised Learning of Dense Shape Correspondence
Oshri Halimi, Or Litany, Emanuele Rodolà +2
We introduce the first completely unsupervised correspondence learning approach for deformable 3D shapes. Key to our model is the understanding that natural deformations (such as c…
Isospectralization, or how to hear shape, style, and correspondence
Luca Cosmo, Mikhail Panine, Arianna Rampini +3
The question whether one can recover the shape of a geometric object from its Laplacian spectrum ('hear the shape of the drum') is a classical problem in spectral geometry with a b…
Functional Maps Representation on Product Manifolds
Emanuele Rodolà, Zorah Lähner, Alex M. Bronstein +2
We consider the tasks of representing, analyzing and manipulating maps between shapes. We model maps as densities over the product manifold of the input shapes; these densities can…
FARM: Functional Automatic Registration Method for 3D Human Bodies
Riccardo Marin, Simone Melzi, Emanuele Rodolà +1
We introduce a new method for non-rigid registration of 3D human shapes. Our proposed pipeline builds upon a given parametric model of the human, and makes use of the functional ma…