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.

Organizer: Jonathan Masci, Emanuele Rodolà, Davide Boscaini, Michael M. Bronstein, Hao Li

Speaker(s):
Emanuele Rodola, University of Lugano
Jonathan Masci, Università della Svizzera italiana
Davide Boscaini, Università della Svizzera italiana
Michael M. Bronstein, Università della Svizzera italiana
Hao Li, University of Southern California

Summary: 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.