3 papers
cs.LG2021
Universal Spectral Adversarial Attacks for Deformable Shapes
Arianna Rampini, Franco Pestarini, Luca Cosmo +2
Machine learning models are known to be vulnerable to adversarial attacks, namely perturbations of the data that lead to wrong predictions despite being imperceptible. However, the…
cs.LG2020
LIMP: Learning Latent Shape Representations with Metric Preservation Priors
Luca Cosmo, Antonio Norelli, Oshri Halimi +2
In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D shapes. Key to our construction is the intro…
cs.CG2018
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…