Computer Graphics
TU Braunschweig

Computer Vision and Machine Learning SS'23
Vorlesung mit Übung

Prof. Dr.-Ing. Martin Eisemann

Hörerkreis: Master

Modul: INF-CG-036, INF-CG-037
Vst.Nr.: 4216036, 4216037

Current information

Prof. Eisemann will no longer teach this course, for the moment. If you look for something similar, try "Bildbasierte Modellierung" by Prof. Magnor. Possibly, the two courses will be merged. Stay tuned.

The repeat exam takes place on February 20th, 2024 in G30. To register follow the instructions below.


Since the course is also offered in the Master Data Science program, the language of the course is English.


After successful completion of this module, students have a basic understanding of the development of complex computer vision applications. They are able to understand computer vision problems and to design and effectively implement suitable (AI-based) solutions.


To participate in the lecture and exercise, you can register on our website under "Teaching -> Course Enrollment" (direct link ).

Studip is not used for this course.

Registration for the exam is done through the Examination Office

Bachelor/Master Note

Upon request (form available from the Examination Office), this course can usually also be taken in the Bachelor's program. Please refer to the module handbook and your examination regulations to see if this course is also regularly offered in your Bachelor's program.


The content may change until the start of the lecture.

- Image Acquisition
- Image Processing Basics
- Deep Learning
- Feature Detectors and Descriptors
- Dense Correspondences / Optical Flow
- Parametric Interpolation
- Epipolar Geometry
- Stereo and Multi-View Reconstruction
- Camera Calibration
- Video Matching
- Morphing and View Interpolation
- Neural Radiance Fields
- Object Detection
- Matting
- Motion Capture
- Machine Learning for Computer Vision Problems
- Computer Vision for Special Effects

Location and Time

LIVE, Tuesdays, 13:15–14:45 / Room IZ 160, please watch the lecture videos BEFORE the session, they will be provided further down weekly )
Weekly, starting 18.04.2023

LIVE, Thursdays, 09:45–11:15 / Room IZ 160, complete exercise sheets BEFORE the presentation, they will be provided further down usually on Thursdays )
Weekly, starting 20.04.2023

Summer semester 29.08.2023, 14:30 - 16:00, Room BI 84.1 (Bienroder Weg 84)
|| Winter semester 20.02.2024, Room G30


The lecture is conducted as Inverted Classroom, i.e. you will have to watch the provided video/material in advance and the lecture time can be used for questions and in-depth subjects.

The material for the next session, as well as the lecture slides, are usually provided here one week in advance.

The password will be given in the lecture and can be requested at if necessary.

LIVE sessions are held regularly every Tuesday, except during field trip week.


18.04.2023 Live Session, Introduction and Image Acquisition [pdf1][pdf2][video] (Image Acquisition can be skipped if you already attended the module "Digital Image Processing" last semester)

25.04.2023 LIVE Session [pdf]

25.04.2023 Digital Image Processing Basics [pdf1][pdf2][video1][video2] (can be skipped if you already attended the module "Digital Image Processing" last semester)

02.05.2023 LIVE Session [pdf]

02.05.2023 Machine Learning Basics [pdf][video] (can be skipped if you already attended the module "Digital Image Processing" last semester)

09.05.2023 LIVE Session [pdf]

09.05.2023 Features [pdf][video]

16.05.2023 Lecture cancelled unfortunately

23.05.2023 LIVE Session [pdf]

23.05.2023 Optical Flow [pdf][video]

29.05. - 02.06.2023 Excursion week (no lecture or exercise)

06.06.2023 LIVE Session [pdf]

06.06.2023 Parametric Transformations and Scattered Data Interpolation [pdf][video]

13.06.2023 LIVE Session [pdf]

13.06.2023 Epipolar Geometry and Stereo [pdf][video]

20.06.2023 LIVE Session [pdf] [slides Deutschlandstipendium]

20.06.2023 Video Matching, Morphing, and View Synthesis [pdf][video]

27.06.2023 is canceled and will be conducted on 04.07. 

27.06.2023 Structure from motion [pdf][video]

04.07.2023 LIVE Session [pdf1][pdf2]

04.07.2023 Neural Radiance Fields [pdf][video]

11.07.2023 Digital Twins and Augmented Reality, guest lecture by Dr. Georgia Albuquerque (DLR) [pdf]

18.07.2023 LIVE Session / Q&A Session [pdf]

22.08.2023 LIVE Session / Q&A Session / Exam preparation


In the exercises, programming will be done in Python with OpenCV and PyTorch.

The exercise tasks will be uploaded Thursdays and discussed on the following Thursday in the exercise session.

The tasks of each exercise sheet must be completed in groups of three to five people and uploaded to the Git repository by Wednesday 23:59 at the latest. Don't forget to include names and matriculation numbers in the repository.

The practical tasks must be demonstrated in the exercise session. Working groups of up to five people are allowed, but everyone in the group must be able to answer any questions about the tasks and the code independently.

The frameworks and solutions have been tested on the computers in the CIP Pool. Unfortunately, we cannot guarantee direct support for other systems. A computer with Linux or Windows is required for completing the tasks. The functionality of the framework under Mac OS/X cannot be guaranteed. If you encounter any issues, please contact us via email at

Kickoff slides [pdf] from the first exercise session.

Sheet 1

[task | solution] —

IDE Setup, Hello World, Debugging
Completion time: 20.04.–03.05.
Presentation: 04.05.

Sheet 2

[task | code | solution] —

Introduction to Python, Image Handling, Color Spaces
Completion time: 04.05.–17.05.
Presentation: 25.05.

Sheet 3

[task | code | solution] —

Noise, Filtering, Image Stacking, Edge Detection, Thresholding
Completion time: 18.05.–07.06.
Presentation: 08.06.

Sheet 4

[task | code | solution] —

Feature Detection, Object Recognition and Detection
Completion time: 01.06.–21.06.
Presentation: 22.06.

Sheet 5

[task | code | solution ] —

Optical Flow
Completion time: 22.06.–05.07.
Presentation: 06.07.

Sheet 6

[task | code | solution] —

Epipolar Geometry, Depth Estimation
Completion time: 06.07.–19.07.
Presentation: 20.07.


The examination date can be found under Place and Time.
Any changes will be announced in the lecture and on this website in time.

  • Exam format: written exam (90 minutes)
  • Certificate acquisition by passing the exam (at least 50% of the points)
  • Requirement for module completion: at least 50% of the points achieved in the exercises.
  • Exam participation is recommended for every course of study!
  • Students must register with the examination office!

Repeat Exam

If you have failed the regular exam or wish to claim a free attempt, you have the opportunity to take the repeat exam.

If that would be your last chance (usually the third) to pass the course, then please continue reading the section "Supplementary Exam"!

In semesters where the course is taught this is simply the regular exam. So all you need to do is register for the regular exam.

In semesters, where the course is not taught, it usually takes place during the first lecture-free week. To register, please sign up for the exam regularly at the examination office and send a corresponding email requesting a repeat exam to, providing your full name, matriculation number, TU email address, number of attempt, and degree program.

The repeat exams are usually held collectively. A date will be communicated to you in due time after registration.

Please note that besides the exam you need to have successfully participated in the exercises to successfully pass the module. The exercises are only offered when the course is held!

Supplementary Exam

If you need an oral supplementary exam because you have not passed the exam in your final regular attempt, please send an email requesting an oral supplementary exam to immediately after receiving the grade (i.e., when it has been officially registered with the examination office), providing your full name, matriculation number, TU email address, and degree program.

The oral supplementary exams are usually held collectively once per semester. A date will be communicated to you in due time beforehand.


  • Programming skills, preferably in Python


  • Richard J. Radke: Computer Vision for Visual Effects,
  • Richard Szeliski: Computer Vision: Algorithms and Applications, Springer Verlag
  • D. Forsyth and J. Ponce: Computer Vision: A Modern Approach. Prentice Hall