View source for 3D Structure From Visual Motion

 Recent news you should be aware of ...
  * The schedule of 2014 lectures is out!
  * A new version of the course is scheduled for the year 2013/2014

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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.

__FORCETOC__

==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 ([http://chrome.ws.dei.polimi.it/index.php/Matt%27s_Home_Page myself]) the course is also held by (in order of appearance)
* [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]
* [http://www.dei.polimi.it/people/caglioti Vincenzo Caglioti]
* [http://www.dei.polimi.it/people/marcon Marco Marcon]
* [http://personal.disco.unimib.it/sorrenti/ Domenico G. Sorrenti]

===Course Schedule===

The course schedule for this yeas edition foresees 3 hour lectures from 14:30 to 17:30 (time might change according to participants needs) of the following days: 12/05/2014, 14/05/2014, 16/05/2014, 19/05/2014, 21/05/2014, 23/05/2014, 26/05/2014, 28/05/2014.

In the following you find a tentative syllabus for the course. 

*'''3d Vision Basics''' 
** Course introduction 
** Feature extraction, matching and tracking
** Projection model and projection matrix 
** Fundamental and Essential matrices
*'''Structure from Motion and Visual Odometry''' 
** Optical flow 
** Combined estiamation of 3D structure and camera egomotion
** Motion extraction and 3D reconstruction 
*'''Unconventional Visual Odometry''' 
** Uncalibrated visual odometry
** Omnidirectional odometry 
*'''Simulataneous Localization and Mapping''' 
** From Bayesian Filtering to SLAM 
** EKF-Based SLAM 
*'''Visual SLAM''' 
** EKF-based Monocular SLAM
** Stereo and Omnidirectional visual SLAM 
** Why filters? PTAM and FrameSLAM 
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*'''3D without 3D''' 
** Plenoptic methods, lumigraph, albedo, non Lambertian surfaces (3h M. Marcon)
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==Course Material & Referencies==

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

===Slides and lecture notes===
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/FeatureTracking.pdf Correspondence analysis and RANSAC] (2011-2012 ed.)
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/MultipleViewGeometry.pdf Camera geometry, single view, and two view geometry material]
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/VisualOdometry.pdf Two view geometry and visual odometry material]
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/FeatureTrackingEgomotion.pdf Optical flow tracking and egomotion estimation]
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/StructureFromMotion.pdf Structure from Motion]
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/BayesianFiltering.pdf Bayesian Filtering, Kalman Filtering, and SLAM]
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/SLAM.pdf Simultaneous Localization and Mapping] a.k.a. SLAM!
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/MonocularSLAM.pdf Monocular SLAM]
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/StereoOmniSLAM.pdf Stereo and Omnidirectional SLAM]
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/PanoramicVisualOdometry.pdf Panoramic Visual Odomentry]
* [http://home.dei.polimi.it/matteucc/lectures/3DSFVM/PTAM.pdf Parallel Tracking and Mapping]
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===Suggested Bibliography===
* R. Hartley, A. Zisserman. [http://www.robots.ox.ac.uk/~vgg/hzbook/index.html Multiple View Geometry in Computer Vision], Cambridge University Press, March 2004.
* S. Thrun, W. Burgard, D. Fox. [http://www.probabilistic-robotics.org/ 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 [http://www.acfr.usyd.edu.au/homepages/academic/tbailey/papers/slamtute1.pdf]
** ''Unified Inverse Depth Parametrization for Monocular SLAM'' by  J.M.M. Montiel, Javier Civera, and Andrew J. Davison [http://www.roboticsproceedings.org/rss02/p11.pdf]
** ''Parallel Tracking and Mapping for Small AR Workspaces'' by Georg Klein and David Murray [http://www.robots.ox.ac.uk/~gk/publications/KleinMurray2007ISMAR.pdf]
** ''FrameSLAM: from Bundle Adjustment to Realtime Visual Mappping'' by Kurt Konolige and Motilal Agrawal [http://www.ai.sri.com/~agrawal/frameslam.pdf]

===Libraries and Demos===

 TBC

== Course Evaluation ==

The course evaluation will be done on the basis of a project which could be completed also in groups of two people. In the case of PhD students this project could/should be somehow related to their research interests.