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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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CVG Group DVL Group

News

02.10.2020

We have five papers accepted to 3DV 2020!

30.09.2020

Our effcient deep network architectures form the AI engine of the project Slow Down COVID-19 at Harvard.

24.07.2020

Our practical course "Vision-based Navigation" (WS18, SS19) by Dr. Vladyslav Usenko and Nikolaus Demmel was honored as best practical course in the academic year 2018/2019 by the department for Informatics.

07.05.2020

We are organizing a workshop on Map-based Localization for Autonomous Driving at ECCV 2020, Glasgow, UK.

13.04.2020

Daniel Cremers received an ERC Advanced Grant (3.5 Mio Euro) for pioneering frontier research from the European Research Council. This constitutes his fifth ERC grant.

More


Practical Course: Learning For Self-Driving Cars and Intelligent Systems (10 ECTS)

Winter Semester 20/21, TU München

Correspondence

Please forward any queries related to the lab to: intellisys-ws20@vision.in.tum.de

Course Registration

Assignment to the lab is done via the matching system. Please additionally send your application documents to the correspondence email. The preliminary meeting to take place on 13 July 2020, 2pm online on https://bbb.vision.in.tum.de/b/qad-bky-6fu. The preliminary meeting slides can be found here .

In addition to applying to the matching system please remember to also send in your application documents latest by 22 July. See the preliminary meeting slides for more details.

Note that the course goes by the name <color #ed1c24><color #ff7f27>Lernbasierte Ansätze für autonome Fahrzeuge und intelligente Systeme</color></color> on the matching system

Course Content

Learning-based approaches have recently made tremendous progress in the computer vision and robotics research community. In this practical course, we will solve challenging real-world problems in the area of self-driving cars and intelligent systems. Topics will be in the direction of perception, control, and environmental understanding. This may also involve working on massive datasets including unstructured or unordered data.

Pre-requisites
  • Good knowledge of the Python language and basic mathematics such as linear algebra, analysis, and numerics is required.
  • Good knowledge of a deep learning framework such as PyTorch, TensorFlow, etc.
  • Participation in at least one of the offered deep learning lectures at TUM is required.
Structure

Students will work in groups of 2 persons on research oriented projects from the beginning of the semester. Assignment of projects to groups will be done in the first week. There will be biweekly presentations to share results/findings among the groups. To review progress and assist with resolving any issues, students are invited to meet the supervisors on a weekly basis.

Schedule

Time: Wednesdays 2-4pm
Room: 02.09.023

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:
CVG Group DVL Group

News

02.10.2020

We have five papers accepted to 3DV 2020!

30.09.2020

Our effcient deep network architectures form the AI engine of the project Slow Down COVID-19 at Harvard.

24.07.2020

Our practical course "Vision-based Navigation" (WS18, SS19) by Dr. Vladyslav Usenko and Nikolaus Demmel was honored as best practical course in the academic year 2018/2019 by the department for Informatics.

07.05.2020

We are organizing a workshop on Map-based Localization for Autonomous Driving at ECCV 2020, Glasgow, UK.

13.04.2020

Daniel Cremers received an ERC Advanced Grant (3.5 Mio Euro) for pioneering frontier research from the European Research Council. This constitutes his fifth ERC grant.

More