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.

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 VT2025

  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 Asgård, both located at the Department of Electrical Engineering.

  Note that the lecture slides that are linked in the schedule below are from last year. They will be updated after each lecture.

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  Date,Time,Room	Activity	Teacher	Literature
  March 31: 10.15-12 Systemet	Lecture 1 Introduction, 3D perception, local features	Per-Erik Forssén	CVAA: 7.1 Background geometry: IREG ch. 7-9,13
  April 1: 13.15-15 Systemet	Lecture 2 Maximum likelihood, RANSAC, cameras and epipolar geometry	Mårten Wadenbäck	Multi-dim course, first half. Also IREG: 17.3 (RANSAC).
  April 3: 8.15-10 Systemet	Lecture 3 Cost minimisation, and robust error norms	Per-Erik Forssén	K. Madsen, NL Least Squares Tutorial Z. Zhang, Parameter Est. Techniques, Ch. 9.
  April 7: 10.15-12 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 15: 13.15-17.00 SU25	CE1 Non-linear optimization and gold standard	Ioannis Athanasiadis	CE1 Lab sheet
  April 22: 13.15-15 Systemet	Lecture 5 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)
  April 28: 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 29: 13.15-17 Asgård	CE2 Dense Correspondences with PatchMatch	Ioannis Athanasiadis	CE2 Lab sheet Skeleton code Cheatsheet notebook
  May 5: 10.15-12 Systemet	Lecture 7 Rotations in 3D	Mårten Wadenbäck	IREG: ch. 11
  May 12: 10.15-11 Systemet	Guest Lecture 1 Maxar Sweden	Erica Strand
  May 12: 11.15-12 Systemet	Guest Lecture 2 Spiideo, Linköping	David Habrman Ludwig Jacobsson
  May 20: 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 22, and ends with a final seminar on May 20. The project builds on top of the two computer exercises.

  For more information see the project description page.