Deep learning on geometric data
(Universita della Svizzera Italiana, Lugano, Switzerland)
The past decade in computer vision research has witnessed the re-emergence of
"deep learning" and in particular, convolutional neural network techniques,
allowing to learn task-specific features from examples and achieving a
breakthrough in performance in a wide range of applications. However, in the
geometry processing and computer graphics communities, these methods are
practically unknown. One of the reasons stems from the facts that 3D shapes
(typically modeled as Riemannian manifolds) are not shift-invariant spaces,
hence the very notion of convolution is rather elusive.
In this talk, I will
show some recent works from our group trying to bridge this gap.
Specifically, I will show the construction of intrinsic convolutional neural
networks on meshes and point clouds, with applications such as finding dense
correspondence between deformable shapes and shape retrieval.