3.7k citations · 7.1k across the 79 of their papers we have counts for
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Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti, Davide Boscaini, Jonathan Masci +3
Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particu…
Geometric deep learning: going beyond Euclidean data
Michael M. Bronstein, Joan Bruna, Yann LeCun +2
Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor network…
Optimization on the biorthogonal manifold
Klaus Glashoff, Michael M. Bronstein
In this paper, we consider optimization problems w.r.t. to pairs of orthogonal matrices . Problems of this form arise in several applications such as finding shape correspo…
Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini, Jonathan Masci, Emanuele Rodolà +1
Establishing correspondence between shapes is a fundamental problem in geometry processing, arising in a wide variety of applications. The problem is especially difficult in the se…
Efficient Globally Optimal 2D-to-3D Deformable Shape Matching
Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt +2
We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D shape represented as a planar curve and a 3D shape represented as a surface; the outpu…