| dc.contributor.author | Bronstein, Michael | en_US |
| dc.contributor.author | Guibas, Leonidas | en_US |
| dc.contributor.author | Kokkinos, Iasonas | en_US |
| dc.contributor.author | Litany, Or | en_US |
| dc.contributor.author | Mitra, Niloy | en_US |
| dc.contributor.author | Monti, Federico | en_US |
| dc.contributor.author | Rodolà, Emanuele | en_US |
| dc.contributor.editor | Jakob, Wenzel and Puppo, Enrico | en_US |
| dc.date.accessioned | 2019-05-05T17:53:33Z | |
| dc.date.available | 2019-05-05T17:53:33Z | |
| dc.date.issued | 2019 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.uri | https://doi.org/10.2312/egt.20191036 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/egt20191036 | |
| dc.description.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. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Deep Learning for Computer Graphics and Geometry Processing | en_US |
| dc.description.seriesinformation | Eurographics 2019 - Tutorials | |
| dc.description.sectionheaders | Tutorials | |
| dc.identifier.doi | 10.2312/egt.20191036 | |
| dc.identifier.pages | 43-43 | |
