Difference between revisions of "3D Structure From Visual Motion"

Revision as of 17:33, 14 October 2011

This is a description page for the PhD course on 3D Structure from Visual Motion: Novel Techniques in Computer Vision and Autonomous Robots/Vehicles. This course can be taken also by students from Computer Engineering in the Laurea Magistrale track.

Course Aim & Organization

Simultaneous estimate of the unknown motion of a camera (or the vehicle this camera is upon) while reconstructing the 3D structure of the observed world is a challenging task that has been deeply studied in the recent literature. The PhD course on 3D Structure from Visual Motion: Novel Techniques in Computer Vision and Autonomous Robots/Vehicles will present modern techniques to simultaneously estimate the unknown motion of a camera while reconstructing the 3D structure of the observed world to be applied in scientific fields such as: 3D reconstruction, autonomous robot navigation, aerial/field surveying, unmanned vehicle maneuvering, etc.

Teachers

Although formally entitled to just one of the teachers ([[1]]) the course is held by (in order of appearance)

• Matteo Matteucci
• Vincenzo Caglioti
• Marco Marcon

With possibly special guests

• Simone Ceriani
• Domenico G. Sorrenti

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 XXX DEI
  • Course introduction (M. Matteucci)
  • Correspondence analysis: tracking and ransac (D. Migliore)
• 17/02/2010 14:30-18:30 in Sala XXX DEI
  • Projection model and projection matrix (V. Caglioti)
  • Fundamental and Essential matrices (V. Caglioti)
• 22/02/2010 14:30-18:30 in Sala XXX DEI
  • Motion extraction and 3D reconstruction (V. Caglioti)
• 24/02/2010 14:30-18:30 in Sala XXX DEI
  • Stereo e Omnidirectional odometry (D. Migliore)
  • Uncalibrated visual odometry (V. Caglioti)
  • Omnidirectional odometry (V. Caglioti)
• 26/02/2010 14:30-18:30 in Sala XXX DEI
  • Optical flow (M. Marcon)
  • Combined estiamation of 3D structure and camera egomotion (M.Marcon)
• 01/03/2010 14:30-18:30 in Sala XXX DEI
  • Bayesian Filtering and SLAM (M. Matteucci)
• 03/03/2010 14:30-18:30 in Sala XXX DEI
  • MonoSLAM, PTAM and FrameSLAM (M. Matteucci, D.G. Sorrenti)
• 05/03/2010 14:30-18:30 in Sala XXX 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

• Camera geometry, single view, and two view geometry material
• Two view geometry and visual odometry material
• Correspondence analysis and RANSAC
• Optical flow tracking and egomotion estimation
• Structure from Motion
• Bayesian Filtering, Kalman Filtering, and SLAM
• Simultaneous Localization and Mapping a.k.a. SLAM!
• Monocular SLAM
• Stereo and Omnidirectional SLAM
• Panoramic Visual Odomentry
• Parallel Tracking and Mapping
• 3D without 3D

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:
  • Simultaneous Localization and Mapping (SLAM): Part I The Essential Algorithms. H. Durrant-Whyte, T. Bailey [2]
  • Unified Inverse Depth Parametrization for Monocular SLAM by J.M.M. Montiel, Javier Civera, and Andrew J. Davison [3]
  • Parallel Tracking and Mapping for Small AR Workspaces by Georg Klein and David Murray [4]
  • FrameSLAM: from Bundle Adjustment to Realtime Visual Mappping by Kurt Konolige and Motilal Agrawal [5]

Course Evaluation