3D Structure From Visual Motion
Recent news you should be aware of ... * 11/04/2012: Rooms for classes updated and first class material published. * 18/03/2012: Detailed course schedule published! * 06/03/2012: Detailed course schedule coming out soon if you did not received the notification email please contact me!
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.
Although formally entitled to just one of the teachers (myself) the course is also held by (in order of appearance)
All lectures are on:
- Friday from 15:15 to 18:15
In the following you find the detailed schedule for the course and the rooms booked for it. In brackets you find also the lecturer for each specific topic.
- 3d Vision Basics (9 hours)
- 30/03/2012 (Sala conferenze - DEI): Course introduction (1h M. Matteucci)
- 30/03/2012 (Sala conferenze - DEI): Feature extraction, matching and tracking (2h M. Matteucci)
- 13/04/2012 (Aula PT1 - Piano terra DEI): Projection model and projection matrix (3h V. Caglioti)
- 20/04/2012 (Aula 3B - Terzo piano DEI): Fundamental and Essential matrices (3h V. Caglioti)
- Structure from Motion and Visual Odometry (6 hours)
- 27/04/2012 (Sala Seminari - Piano terra DEI): Optical flow (1.5h M. Marcon)
- 27/04/2012 (Sala Seminari - Piano terra DEI): Combined estiamation of 3D structure and camera egomotion (1.5h M.Marcon)
- 04/05/2012 (Sala Seminari - Piano terra DEI): Motion extraction and 3D reconstruction (3h V. Caglioti)
- Unconventional Visual Odometry (6 hours)
- 11/05/2012 (Sala Seminari - Piano terra DEI): Uncalibrated visual odometry (1.5h V. Caglioti)
- 11/05/2012 (Sala Seminari - Piano terra DEI): Omnidirectional odometry (1.5h V. Caglioti)
- 18/05/2012 (Sala Seminari - Piano terra DEI): Stereo e Omnidirectional odometry (3h M. Matteucci)
- Simulataneous Localization and Mapping (3 hours)
- 25/05/2012 (Aula PT1 - Piano terra DEI): From Bayesian Filtering to SLAM (1.5h M. Matteucci)
- 25/05/2012 (Aula PT1 - Piano terra DEI): EKF-Based SLAM (1.5h M. Matteucci)
- Visual SLAM (6 hours)
- 01/06/2012 (Aula PT1 - Piano terra DEI): EKF-based Monocular SLAM (3h D.G. Sorrenti)
- 08/06/2012 (Sala Seminari - Piano terra DEI): Why filters? PTAM and FrameSLAM (3h M. Matteucci)
- 3D without 3D (3 hours)
- 15/06/2012 (Sala Seminari - Piano terra DEI): Plenoptic methods, lumigraph, albedo, non Lambertian surfaces (3h M. Marcon)
Course Material & Referencies
The following is some suggested material to follow the course lectures.
Slides and lecture notes
- Correspondence analysis and RANSAC (2011-2012 ed.)
- Camera geometry, single view, and two view geometry material
- Two view geometry and visual odometry material
- 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
- 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 
- Unified Inverse Depth Parametrization for Monocular SLAM by J.M.M. Montiel, Javier Civera, and Andrew J. Davison 
- Parallel Tracking and Mapping for Small AR Workspaces by Georg Klein and David Murray 
- FrameSLAM: from Bundle Adjustment to Realtime Visual Mappping by Kurt Konolige and Motilal Agrawal 
Libraries and Demos
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.