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

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Presentation transcript:

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

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)

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.

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

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

Application Google Street View

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

Applications: Robot Navigation

Applications 3D modeling

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

Contact information Dezhen Song, HRBB 311C Tel