Physics Department, TUM | Course 0000000538 in WS 2022/3

General Data

Course Type

lecture

Semester Weekly Hours

2 SWS

Organisational Unit

Informatics 28 - Associate Professorship of Visual Computing (Prof. Nießner)

Lecturers

Matthias Nießner

Dates

Wed, 10:00–12:00, virtuell

iCalendar

	Date and Time	Room and Remark
	Wed, 2022-10-19, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2022-10-26, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2022-11-09, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2022-11-16, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2022-11-23, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2022-11-30, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2022-12-07, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2022-12-14, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2022-12-21, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2023-01-11, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2023-01-18, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2023-01-25, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.
	Wed, 2023-02-01, 10:00–12:00	Speakers: Barbara Rössle M.Sc., Yinyu Nie Ph.D., Zhenyu Chen M.Sc., David Rozenberszki M.Sc., Junwen Huang M.Sc.

Assignment to Modules

IN2390: Advanced Deep Learning for Computer Vision: Visual Computing / Advanced Deep Learning for Computer Vision: Visual Computing
This module is included in the following catalogs:
- Catalogue of non-physics elective courses

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	The lectures will provide extensive theoretical aspects of neural networks and in particular deep learning architectures, specifically for advanced methods in the field of Computer Vision. The practical sessions will be key, students shall get familiar with Deep Learning through hours of training and testing. They will work with PyTorch and implement advanced network architectures. The project will have a focus on visual computing, including the following topics: - neural rendering - generative neural networks (GANs) - neural radiance fields - deep fake generation - media forensics (forgery detection) - scene reconstruction (multi-view, depth sensors, etc.) - generative geometric models - semantic scene understanding (object detection, instance segmentation, semantic segmentation) - 3D scene understanding for autonomous driving (e.g., with Lidar/Radar) - reinforcement learning (e.g., for 3d modeling, 3d auto-scanning, 3d navigation) - natural language processing (NLP) for scene understanding We recommend to take a look at the recent list of publications at https://niessnerlab.org/ to get a better idea of recent research projects.
Links	E-Learning course (e. g. Moodle) TUMonline entry

additional remarks

The lectures will provide extensive theoretical aspects of neural networks and in particular deep learning architectures, specifically for advanced methods in the field of Computer Vision. The practical sessions will be key, students shall get familiar with Deep Learning through hours of training and testing. They will work with PyTorch and implement advanced network architectures. The project will have a focus on visual computing, including the following topics: - neural rendering - generative neural networks (GANs) - neural radiance fields - deep fake generation - media forensics (forgery detection) - scene reconstruction (multi-view, depth sensors, etc.) - generative geometric models - semantic scene understanding (object detection, instance segmentation, semantic segmentation) - 3D scene understanding for autonomous driving (e.g., with Lidar/Radar) - reinforcement learning (e.g., for 3d modeling, 3d auto-scanning, 3d navigation) - natural language processing (NLP) for scene understanding We recommend to take a look at the recent list of publications at https://niessnerlab.org/ to get a better idea of recent research projects.

Links

E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

Semester	Title	Lecturers	Dates
SS 2024	Advanced Deep Learning for Computer Vision: Visual Computing (IN2390)	Chen, Y. Erkoc, Z. Li, L. Nießner, M. Rössle, B. … (total 7)	Wed, 08:00–12:00, MI 01.09.014
WS 2023/4	Advanced Deep Learning for Computer Vision: Visual Computing (IN2390)	Erkoc, Z. Li, L. Nießner, M. Rössle, B. Rozenberszki, D. … (total 6)	Wed, 10:00–12:00, MI 01.09.014
SS 2023	Advanced Deep Learning for Computer Vision: Visual Computing (IN2390)	Chen, Z. Franzmann, A. Nie, Y. Nießner, M. Rössle, B. … (total 6)	Wed, 10:00–12:00, MI 01.09.014
SS 2022	Advanced Deep Learning for Computer Vision: Visual Computing (IN2390)	Nießner, M.	Wed, 10:00–11:00, virtuell
WS 2021/2	Advanced Deep Learning for Computer Vision: Visual Computing (IN2390)	Nießner, M.	Mon, 10:00–12:00, virtuell

Advanced Deep Learning for Computer Vision: Visual Computing (IN2390)

Course 0000000538 in WS 2022/3

General Data

Assignment to Modules

Further Information

Equivalent Courses (e. g. in other semesters)