Practical Course: Deep Learning for 3D Perception (IN2106, IN4319)
Course 0000000788 in SS 2023
General Data
Course Type | practical training |
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Semester Weekly Hours | 6 SWS |
Organisational Unit | Informatics 15 - Chair of Computer Graphics and Visualization (Prof. Westermann) |
Lecturers |
Angela Dai Christian Diller Assia Franzmann Can Gümeli Jiapeng Tang Susanne Weitz |
Dates |
Wed, 12:00–14:00, MI 02.13.010 |
Assignment to Modules
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IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
additional remarks | This course is equivalent to Guided Research in the 3D AI Lab. We will take an academic research focus on cutting-edge topics in computer vision, graphics, and machine learning. Topics cover 3D reconstruction, 3D semantic scene understanding, generative 3D modeling, dynamic modeling, self-supervised, weakly-supervised, and few-shot learning for 3D reconstruction and semantics. Please send applications (CV + Transcripts) to angela.dai@tum.de |
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Links |
E-Learning course (e. g. Moodle) TUMonline entry |
Equivalent Courses (e. g. in other semesters)
Semester | Title | Lecturers | Dates |
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SS 2024 | Practical Course: Deep Learning for 3D Perception (IN2106, IN4319) | Benedi San Millan, M. Dai, A. Gümeli, C. Rozenberszki, D. Weitz, S. |
Fri, 14:00–16:00, MI 02.13.010 |
WS 2023/4 | Practical Course: Deep Learning for 3D Perception (IN2106, IN4319) | Dai, A. Weitz, S. |
Mon, 16:00–18:00, MI 02.13.010 |
WS 2022/3 | Practical Course: Deep Learning for 3D Perception (IN2106, IN4319) | Dai, A. Franzmann, A. Weitz, S. |
Mon, 16:00–18:00, MI 02.13.010 |
SS 2022 | Practical Course: Deep Learning for 3D Perception (IN2106, IN4319) | Dai, A. Franzmann, A. Weitz, S. |
Wed, 12:00–14:00, MI 02.13.010 |