“Geometric Deep Learning” Chaired by Hao Li, Michael Bronstein, Davide Boscaini, Emanuele Rodolà and Jonathan Masci
Conference:
Type(s):
Title:
- Geometric Deep Learning
Organizer(s):
Presenter(s)/Author(s):
Abstract:
The purpose of this course is to overview the foundations and the current state of the art on learning techniques for 3D shape analysis. Special focus will be put on deep learning techniques (CNN) applied to Euclidean and non-Euclidean manifolds for tasks of shape classification, retrieval and correspondence.
Additional Information:
Level: Beginner
Prerequisites: The course will assume no particular background, beyond some basic working knowledge that is a common denominator for people in the field of computer graphics. All the necessary notions and mathematical foundations will be described.
Presentation Language: English
Intended Audience: The course is targeted to graduate students, practitioners, and researchers interested in shape analysis, matching, retrieval, and big data.
