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Archived teaching schedules 2015–2016
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TIETS33 Practical Introduction to 3D Computer Vision 3 ECTS
Period I Period II Period III Period IV
Language of instruction
Type or level of studies
Advanced studies
Course unit descriptions in the curriculum
Degree Programme in Computer Sciences
School of Information Sciences

Learning outcomes

During the course the students learn to combine and apply basic results, algorithms, and methodology of 3D computer vision to solve vision problems in practical applications.

General description

A short hands-on course about the basic results and methodology of 3D computer vision, focusing on geometric results and optimization, and their practical applications in, for example, augmented reality, motion capture, robotics, and vision-based interaction techniques.

Projective geometry: homogeneous coordinates, perspective projection, single view geometry, absolute pose problem (DLT & P3P), camera calibration, two view geometry, relative pose problem, triangulation. (4+4h) 

Estimation: Feature detection & matching, random sample consensus, Levenberg-Marquardt optimization, automatic differentiation, large-scale sparse optimization (bundle adjustment), 3D reconstruction from multiple views or video. (4+4h)

Dense 3D: Dense stereo, depth cameras (e.g. Kinect), point clouds, iterative closest point algorithm. (2+2h)

Applications & open source libraries for computer vision (exercises & project)

Enrolment for University Studies

Enrolment time has expired


Timo Tossavainen, PhD, Teacher responsible


30-Oct-2015 – 30-Dec-2015
Lectures and excercies
Fri 30-Oct-2015 at 10-12, Pinni B1084, lecture
Fri 6-Nov-2015 - 27-Nov-2015 weekly at 10-15, Pinni B1084, 10-12 exercises and 13-15 lectures
Fri 11-Dec-2015 at 10-12, Pinni B1084, exercises


Numeric 1-5.

Evaluation criteria

Coursework exercises (75%) and a short computer vision project (25%), no exam.

Study materials

Course notes, slides, and selected papers. The course covers short selected
parts of

  • R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision, 2nd ed, Cambridge University Press, 2003.
  • R. Szeliski. Computer Vision, Algorithms and Applications, Springer, 2011.
  • A. Griewank, A. Walther. Evaluating Derivatives. Principles and Techniques of Algorithmic Differentiation, 2nd ed, SIAM, 2008.

Further information

This course can be included on the modules

The course focuses on solving practical problems rather than on theory. Basics of programming (C++, Python, or Octave/Matlab), linear algebra (working with small matrices e.g. in computer graphics), calculus (derivatives), and probability (counting combinations, normal distribution) suffice.

Language of Instruction:
Lectures in Finnish or English depending on enrolled students. The course material is in English.