1. Multiple View Geometry in Computer Vision Slides modified from Marc Pollefeys’ online course materials Lecturer: Prof. Dezhen Song

2. Multiple View Geometry a b c A (a,b)  A (a,b)  c f(a,b,c)=0 a b c (a,b,c)  (a,b,c) (reconstruction) (calibration) (transfer)

3. Course objectives To understand the geometric relations between multiple views of scenes. To understand the general principles of parameter estimation. To be able to compute scene and camera properties from real world images using state-of-the-art algorithms.

4. Relation to other vision/image courses Focuses on geometric aspects No image processing CPSC 689: Pattern Recognition (by Dr. Gutierrez-Osuna) ECEN 447 - Digital Image Processing (by Dr. Braga-Neto)

5. Material Textbook: Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman, 2 nd Edition Cambridge University Press

6. Application Google Street View

7. Applications MatchMoving Compute camera motion from video (to register real an virtual object motion)

8. Applications: Robot Navigation

9. Applications 3D modeling

10. Content Background: Projective geometry (2D, 3D), Parameter estimation, Algorithm evaluation. Single View: Camera model, Calibration, Single View Geometry. Two Views: Epipolar Geometry, 3D reconstruction, Computing F, Computing structure, Plane and homographies. Three Views: Trifocal Tensor, Computing T. More Views: N-Linearities, Multiple view reconstruction, Bundle adjustment, auto- calibration, Dynamic SfM

11. Contact information Dezhen Song, HRBB 311C dzsong@cs.tamu.edu Tel. 845-5464