Deep Learning for Computer Graphics and Geometry Processing

Michael M. Bronstein, Leonidas J. Guibas, Iasonas Kokkinos, Or Litany, Niloy J. Mitra, Federico Monti, Emanuele Rodolà

Abstract: In computer graphics and geometry processing, many traditional problems are now becoming increasingly handled by data-driven methods. In an increasing variety of problem settings, deep networks are state-of-the-art, beating dedicated hand-crafted methods by significant margins. This tutorial gives an organized overview of core theory, practice, and graphics-related applications of deep learning.