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| Anno Accademico |
2023/2024 |
| Corso di Studi |
Dott. - MI (1380) Ingegneria dell'Informazione / Information Technology |
| Anno di Corso |
1 |
| Codice Identificativo |
061636 |
| Denominazione Insegnamento |
ADVANCED DEEP LEARNING MODELS AND METHODS FOR 3D SPATIAL DATA |
| Tipo Insegnamento |
MONODISCIPLINARE |
| Crediti Formativi Universitari (CFU) |
5.0 |
| Programma sintetico |
Nowadays deep learning spans multiple fields in science and engineering, from autonomous driving to human machine interaction, achieving human performance in solving many complex tasks, such as natural language processing and image recognition. This course presents recent advances in deep learning that brought data-driven models to achieve state-of-the-art performance in solving 3D vision problems. In particular, students will become acquainted with the biggest challenges of handling 3D data that are scattered in nature, thus are not suited for traditional filtering operations underpinning convolutional layers. The course will illustrate the most important layers for handling 3D data, as well as the neural networks for solving 3D Computer Vision problems and their application to Robotics and Computational Geometry.
This is intended as an advanced course, thus proficiency in neural networks, convolutional neural networks and basic notions of optimization are assumed as pre-requirement to the participants. |
| Settori Scientifico Disciplinari (SSD) |
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Scaglione
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Nome
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Programma dettagliato
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Da (compreso)
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A (escluso)
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A
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ZZZZ
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Boracchi Giacomo, Magri Luca, Matteucci Matteo, Melzi Simone
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