Learn about 3D reconstruction from images as well as computer vision methods needed for mobile robotics.
All the lectures are planned to be held on-site in lecture room i11 Recordings of previous lecture topics are available on tube.
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 organized as on-site events.
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.
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 and requires Python programming
The assignments will be introduced in handout sessions as on-site events. A tutor will be available to assist you with the assignments and he will hold on-site Q&A sessions. Each group also will be scheduled for an assignment interview after the submission of an assignment. A schedule for the assignment interviews will be available in the TC. The schedule for the handout, sessions and Q&A sessions can be seen in TUG-online.
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.
(slides will gradually appear here)
(website updated on 27.02.2024)