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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich



Computer Vision I: Variational Methods

WS 2018/19, TU München



  • The exam review for the first exam will take place on Monday, March 11 at 11am in room 02.09.023. If you have questions regarding the preliminary exam results, do not contact Mrs. Wagner, but rather the lecture's tutors at cvvm-ws18@vision.in.tum.de.

Location: Interims Hörsaal 2 (5620.01.102)
Time and Date:
Tuesday, 10.15h - 11.45h
Thursday, 10.15h - 11.00h
Lecturer: Prof. Dr. Daniel Cremers

The lectures are held in English.


Location: Interims II (at the chemistry building): 004, Hörsaal 1 (5416.01.004)
Time and Date: Wednesday, 10.30h - 12.30h
Organization: Marvin Eisenberger, Mohammed Brahimi
Contact: cvvm-ws18@vision.in.tum.de


Location: 00.02.001, MI HS 1, Friedrich L. Bauer Hörsaal (5602.EG.001)
Time and Date: 12.02.2019, 10.30h - 12.30h
You may only use standard writing materials. No cheat sheet, no electronic devices.
Exam review: 11.03.2019 at 11am in room 02.09.023.


Location: 102, Interims Hörsaal 2 (5620.01.102)
Time and Date: 11.04.2019, 10.30h - 12.30h
You may only use standard writing materials. No cheat sheet, no electronic devices.


Variational Methods are among the most classical techniques for optimization of cost functions in higher dimension. Many challenges in Computer Vision and in other domains of research can be formulated as variational methods. Examples include denoising, deblurring, image segmentation, tracking, optical flow estimation, depth estimation from stereo images or 3D reconstruction from multiple views.

In this class, I will introduce the basic concepts of variational methods, the Euler-Lagrange calculus and partial differential equations. I will discuss how respective computer vision and image analysis challenges can be cast as variational problems and how they can be efficiently solved. Towards the end of the class, I will discuss convex formulations and convex relaxations which allow to compute optimal or near-optimal solutions in the variational setting.


The requirements for the class are knowledge in basic mathematics, in particular multivariate analysis and linear algebra. Some prior knowledge on optimization is a plus but is not necessary.

Lecture Material

Slides and exercise sheets can be accessed here.
For password request, please contact us using your TUM email address.


A previous (very similar) version of this course was recorded in 2013. The videos can be found on Youtube.

Rechte Seite

Informatik IX
Chair of Computer Vision & Artificial Intelligence

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

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