INP.32888UF+INP.32989UF Robot vision (2 VO+1 KU, SS)

Learn about 3D reconstruction from images as well as computer vision methods needed for mobile robotics.

Important information for SS 2021

All the lectures will be online. The first lecture will happen as a streaming event, further lectures will be pre-recorded videos which can be accessed at any time. There will be Q&A sessions as live stream for direct interaction. To get access information to the online lectures students need to be registered. The information can then be found in the Teach Center.

The practical is organized as programming assignments which can be carried out from home. The practical is organized as group work in groups of 2. This is important such that you do have the possibility to discuss the topics of the practical with a peer. Assignment handouts will be held as streaming events. There will be also online Q&A sessions with a student assistent.

Course description

The course is organized as a lecture and practical. It is advised to take both of them at the same time. To pass the lecture an exam has to be taken. Exam dates will be published in TUG-Online. The practical part is organized as programming assignments and carried out in groups of 2.

Lecture topics

  • Projective geometry
  • Image formation and camera calibration
  • Geometric algorithms (Fundamental matrix, Essential Matrix, Triangulation)
  • Robust estimation (Ransac)
  • Features and matching
  • SfM
  • Bundle adjustment
  • Stereo matching
  • Deep learning for depth estimation
  • Depth cameras

Practical part (INP.32989UF)

The practical consists of programming assignments. The practical will be done as group work in groups of 2 students. There will be 3 assignments to be completed through the semester. The assignments need to be programmed in C/C++ with the help of the OpenCV computer vision library. The first assignment will be about camera calibration. The second assignment will be about feature matching and 2 view geometry estimation. The third assignment will be depth estimation using deep learning.

There will not be weekly meetings for the practical. There will however be tutor hours on selected dates.

The assignments will be available in the teach center and will also be handed out and discussed in the lecture slots.

Assignment schedule:

  • Assignment 1
    • Handout: 10.3.2021 (online streaming event)
    • Deadline: 27.4.2021
    • Interviews: 12.5.2021
  • Assignment 2
    • Handout: 24.3.2021 (online streaming event)
    • Deadline: 18.5.2021
    • Interviews: 26.5.2021
  • Assignment 3
    • Handout: 5.5.2021 (online streaming event)
    • Deadline: 15.6.2021
    • Interviews: 23.6.2021


Grades for the lecture can be obtained by taking a written exam. Exam dates will be published in TUG-Online. The grades for the practical (INP.32989UF) are independent of the lecture and will be determined based on the submitted programming assignments.

Times and dates:

  • First lecture will be an online streaming event using Webex on the 2.3.2021 from 14:30-16:00 (Webex link can be found in the Teach Center Forum).
  • First event for the practical (RV KU) will be on 3.3.2021 as an online streaming event. Topic will be an introduction to the organization of the practical.
  • The main exam date will be at the time of the last lecture slot 29.6.2021 from 14:30-16:00 as a written exam online.

Lecture slides: 

(slides will gradually appear here)


Lecture slides 2020: 



(website updated on 24.02.2021)