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Multiple View Geometry in Computer Vision

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Presentation on theme: "Multiple View Geometry in Computer Vision"— Presentation transcript:

1 Multiple View Geometry in Computer Vision
Marc Pollefeys Comp

2 Multiple View Geometry
A a a c c b f(a,b,c)=0 b (a,b) A (reconstruction) (a,b,c) (a,b,c) (calibration) (a,b) c (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 Comp 254: Image Processing an Analysis Mostly orthogonal to this course, complementary Comp 256: Computer Vision (fall 2003) Will be much broader, based on new book: “Computer Vision: a modern approach” David Forsyth and Jean Ponce

5 Material Textbook: Multiple View Geometry in Computer Vision
by Richard Hartley and Andrew Zisserman Cambridge University Press Alternative book: The Geometry from Multiple Images by Olivier Faugeras and Quan-Tuan Luong MIT Press On-line tutorial:

6 Learning approach read the relevant chapters of the books and/or reading assignements before the course.   In the course the material will then be covered in detail and motivated with real world examples and applications.  Small hands-on assignements will be provided to give students a "feel" of the practical aspects. Students will also read and present some seminal papers to provide a complementary view on some of the covered topics. Finally, there will also be a project where students will implement an algorithm or approach using concepts covered by the course.    Grade distribution Class participation: 20% Hands-on assignments: 10% Paper presentation: 10% Implementation assignment/project: 40% Final: 20%

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

8 Applications 3D modeling

9 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, Cheirality, Duality

10 Multiple View Geometry course schedule (tentative)
Jan. 7, 9 Intro & motivation Projective 2D Geometry Jan. 14, 16 (no course) Jan. 21, 23 Projective 3D Geometry Parameter Estimation Jan. 28, 30 Algorithm Evaluation Feb. 4, 6 Camera Models Camera Calibration Feb. 11, 13 Single View Geometry Epipolar Geometry Feb. 18, 20 3D reconstruction Fund. Matrix Comp. Feb. 25, 27 Structure Comp. Planes & Homographies Mar. 4, 6 Trifocal Tensor Three View Reconstruction Mar. 18, 20 Multiple View Geometry MultipleView Reconstruction Mar. 25, 27 Bundle adjustment Papers Apr. 1, 3 Auto-Calibration Apr. 8, 10 Dynamic SfM Apr. 15, 17 Cheirality Apr. 22, 24 Duality Project Demos

11 Fast Forward! Quick overview of what is coming…

12 Background La reproduction interdite (Reproduction Prohibited), 1937, René Magritte.

13 Projective 2D Geometry Points, lines & conics Transformations
Cross-ratio and invariants

14 Projective 3D Geometry Points, lines, planes and quadrics
Transformations П∞, ω∞ and Ω ∞

15 Estimation How to compute a geometric relation from correspondences, e.g. 2D trafo Linear (normalized), non-linear and Maximum Likelihood Estimation Robust (RANSAC)

16 Evaluation and error analysis
How good are the results we get Bounds on performance Covariance propagation & Monte-Carlo estimation error residual

17 Single-View Geometry The Cyclops, c. 1914, Odilon Redon

18 Camera Models Mostly pinhole camera model
but also affine cameras, pushbroom camera, …

19 Camera Calibration Compute P given (m,M) Radial distortion
(normalized) linear, MLE,… Radial distortion

20 More Single-View Geometry
Projective cameras and planes, lines, conics and quadrics. Camera center and camera rotation Camera calibration and vanishing points, calibrating conic and the IAC

21 Single View Metrology Antonio Criminisi

22 Two-View Geometry The Birth of Venus (detail), c. 1485, Sandro Botticelli

23 Epipolar Geometry Fundamental matrix Essential matrix

24 Two-View Reconstruction

25 Epipolar Geometry Computation
(normalized) linear: minimal: MLE: RANSAC … and automated two view matching

26 Rectification Warp images to simplify epipolar geometry

27 Structure Computation
Points: Linear, optimal, direct optimal Also lines and vanishing points

28 Planes and Homographies
Relation between plane and H given P and P’ Relation between H and F, H from F, F from H The infinity homography H∞

29 Three-View Geometry The Birth of Venus (detail), c. 1485, Sandro Botticelli

30 Trifocal Tensor

31 Three View Reconstruction
(normalized) linear minimal (6 points) MLE (Gold Standard)

32 Multiple-View Geometry
The Birth of Venus (detail), c. 1485, Sandro Botticelli

33 Multiple View Geometry
Quadrifocal tensor 81 parameters, but only 29 DOF!

34 Multiple View Reconstruction
Affine factorization Projective factorization

35 Multiple View Reconstruction
Sequential reconstruction

36 Bundle Adjustment Maximum Likelyhood Estimation
for complete structure and motion U1 U2 U3 WT W V P1 P2 P3 M 12xm 3xn (in general much larger)

37 Bundle Adjustment Maximum Likelyhood Estimation
for complete structure and motion WT V U-WV-1WT 11xm 3xn

38 (including radial distortion)
Bundle adjustment No bundle adjustment Bundle adjustment needed to avoid drift of virtual object throughout sequence Bundle adjustment (including radial distortion)

39 Auto-calibration * * projection constraints

40 Dynamic Structure from Motion

41 Cheirality Oriented projective geometry
Allows to use fact that points are in front of camera to recover quasi-affine reconstruction to determine order for image warping to determine orientation for rectification with epipoles in images etc.

42 Duality Gives possibility to interchange role of P and X in algorithms

43 Contact information Marc Pollefeys, Room 205 Tel


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