3D Structure From Visual Motion

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Course Aim & Organization

This is a description page for the PhD course on 3D Structure from Visual Motion: Novel Techniques in Computer Vision and Autonomous Robots/Vehicles. It is meant to present modern techniques to simultaneously estimate the unknown motion of a camera while reconstructing the 3D structure of the observed world to be applied un scientific fields such as: 3D reconstruction, autonomous robot navigation, aerial/field surveying, unmanned vehicle maneuvering, etc.


Although formally entitled to just one of the teachers the course is held by (in order of appearance)

Course Schedule

This is the schedule foreseen for the course. The timing refers to the duration of the room reservation not necessarily the duration of the lecture ;-)

  • 15/02/2010 14:30-18:30 in Sala Seminari DEI
    • Course introduction (M. Matteucci)
    • Correspondence analysis: tracking and ransac (D. Migliore)
    • Optical flow (D. Migliore)
  • 17/02/2010 14:30-18:30 in Aula PT1 DEI
    • Projection model and projection matrix (V. Caglioti)
    • Fundamental and Essential matrices (V. Caglioti)
  • 22/02/2010 14:30-18:30 in Sala Seminari DEI
    • Motion extraction and 3D reconstruction (V. Caglioti)
  • 24/02/2010 14:30-18:30 in Sala Seminari DEI
    • Uncalibrated visual odometry (V. Caglioti)
    • Omnidirectional odometry (V. Caglioti)
  • 26/02/2010 14:30-18:30 in Sala Seminari DEI
    • Combined estiamation of 3D structure and camera egomotion (M.Marcon)
  • 01/03/2010 14:30-18:30 in Aula PT1
    • Bayesian Filtering and SLAM (M. Matteucci)
  • 03/03/2010 14:30-18:30 in Aula PT1 DEI
    • MonoSLAM, PTAM and FrameSLAM (M. Matteucci, D.G. Sorrenti)
  • 05/03/2010 14:30-18:30 in Sala Seminari DEI
    • 3D without 3D: plenoptic methods, lumigraph, albedo, non Lambertian surfaces (M. Marcon)

Course Material & Referencies

The following is some suggested material to follow the course lectures.

Slides and lecture notes

Suggested Bibliography

  • R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision, Cambridge University Press, March 2004.
  • S. Thrun, W. Burgard, D. Fox. Probabilistic Robotics, MIT Press, September 2005.
  • Papers you might find useful to deepen your study:
    • 'FrameSLAM: from Bundle Adjustment to Realtime Visual Mappping' by Kurt Konolige and Motilal Agrawal [1]