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TSBB33 3D Computer Vision
This course covers the algorithms and estimation problems used to infer 3D structure from images. The course covers both the mathematics used, and how these are put into practice in algorithm implementation. The course features two computer labs, and a 3hp project where groups of participants together will implement a structure from motion system. The project is presented both in written form and orally at a seminar. The course ends with a written exam on the theoretical content.
New course! This course will run for the first time in spring 2023. It is based on the geometry and multi-view stereo parts of the course TSBB15 Computer Vision.
General Information
- Course syllabus
Course syllabus can be found in the Study guide.
- Schedule
The course schedule in TimeEdit.
- Literature
- K. Nordberg, Introduction to Representations and Estimation in Geometry (IREG). [Book webpage]
- R. Szeliski, Computer Vision, Algorithms and Applications (CVAA).
Available as an on-campus e-book via the LiU library. [Book webpage] - R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (HZ), on-campus e-book
- Additional material can be found in the course repository.
- Another recent book that may interest you (we will not use this
one in the course):
K. Ikeuchi, Computer Vision, a Reference Guide, on-campus e-book.
Lecture Schedule VT2023
Below is an up-to-date list of all staffed lectures and
computer labs.
Additional, unstaffed computer lab time can be
found in TimeEdit.
All lectures are given in Systemet and all labs are in Olympen, both located at the Department of Electrical Engineering.
| Date,Time,Room | Activity | Teacher | Literature |
|---|---|---|---|
|
March 27: 10.15-12 Systemet |
Lecture 1 Introduction, 3D perception, local features |
Per-Erik Forssén | CVAA: 7.1 Background geometry: IREG ch. 7-9,13 |
|
March 28: 13.15-15 Systemet |
Lecture 2 Maximum likelihood, RANSAC, the essential matrix |
Mårten Wadenbäck | IREG: 10.5 (E), 15.4 (PnP), 16.3 (E est.) |
|
March 30: 08.15-10 Systemet |
Lecture 3 Non-linear least squares estimation, and robust error norms |
Per-Erik Forssén | K. Madsen, NL
Least Squares Tutorial Z. Zhang, Parameter Est. Techniques, Ch. 9. |
|
April 11: 13.15-17.00 Olympen |
CE1 Non-linear optimization and gold standard |
Johan
Edstedt Ioannis Athanasiadis |
CE1 Lab sheet |
|
April 13: 08.15-10 Systemet |
Lecture 4 Multiview stereo, correspondence fields, and triangulation |
Per-Erik Forssén | Furukawa
MVS-tutorial, PatchMatch paper IREG: 10.4, CVAA: 12.1, 12.3 Jump Flooding paper |
|
April 17: 10.15-12 Systemet |
Lecture 5 Representation of 3D rotations |
Mårten Wadenbäck | IREG: ch. 11 |
|
April 24: 10.15-12 Systemet |
Lecture 6 Absolute and relative camera pose, and minimal cases |
Mårten Wadenbäck | CVAA: 8.1.5, 11.2 IREG: ch. 15 |
|
April 25: 17.15-21 Olympen |
CE2 Densification with PatchMatch |
Johan
Edstedt Ioannis Athanasiadis |
CE2
Lab sheet Skeleton code Cheatsheet notebook |
|
April 27: 8.15-10 Systemet |
Lecture 7 Structure from motion, bundle adjustment, and project start |
Per-Erik Forssén | IREG: 15.1,15.12 (3D alignment), 21 (SfM) CVAA: 8.1.5, 8.3.1 (3D alignment), 11 (SfM) |
|
May 8: 10.15-11 Systemet |
Guest Lecture 1 Spotscale |
Martin Svensson | |
|
May 15: 10.15-11 Systemet |
Guest Lecture 2 Maxar Sweden |
Leif Haglund | |
|
May 23: 13.15-17 Systemet |
Final Seminar Project presentations, exam discussion, and wrap up |
Per-Erik Forssén |
3D reconstruction project
Half the course consists of a 3D reconstruction project. The project starts on April 27, and ends with a final seminar on May 23. The project builds on top of the two computer exercises.
For more information see the project description page.