37 citations · 40 across the 3 of their papers we have counts for
3 papers
cs.CV2020★ 2 cited
3D Shape Segmentation with Geometric Deep Learning
Davide Boscaini, Fabio Poiesi
The semantic segmentation of 3D shapes with a high-density of vertices could be impractical due to large memory requirements. To make this problem computationally tractable, we pro…
cs.CV2016★ 37 cited
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…
cs.CV2014★ 1 cited
Shape-from-intrinsic operator
Davide Boscaini, Davide Eynard, Michael M. Bronstein
Shape-from-X is an important class of problems in the fields of geometry processing, computer graphics, and vision, attempting to recover the structure of a shape from some observa…