3D Structure From Visual Motion
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.
Contents
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 (myself) the course is also held by (in order of appearance)
With possibly special guests
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 [1]
- Unified Inverse Depth Parametrization for Monocular SLAM by J.M.M. Montiel, Javier Civera, and Andrew J. Davison [2]
- Parallel Tracking and Mapping for Small AR Workspaces by Georg Klein and David Murray [3]
- FrameSLAM: from Bundle Adjustment to Realtime Visual Mappping by Kurt Konolige and Motilal Agrawal [4]
