Epipolar Geometry Class 7 - PowerPoint PPT Presentation

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Epipolar Geometry Class 7

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Title: Multiple View Geometry in Computer Vision Author: pollefey Last modified by: pollefey Created Date: 1/7/2003 2:47:06 PM Document presentation format – PowerPoint PPT presentation

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Title: Epipolar Geometry Class 7


1
Epipolar GeometryClass 7
  • Read notes 3.2.1

2
Feature tracking
  • Tracking
  • Multi-scale
  • Good features
  • Feature monitoring

Transl.
Affine transf.
3
Three questions
  1. Correspondence geometry Given an image point x
    in the first image, how does this constrain the
    position of the corresponding point x in the
    second image?
  • (ii) Camera geometry (motion) Given a set of
    corresponding image points xi ?xi, i1,,n,
    what are the cameras P and P for the two views?
  • (iii) Scene geometry (structure) Given
    corresponding image points xi ?xi and cameras
    P, P, what is the position of (their pre-image)
    X in space?

4
The epipolar geometry
C,C,x,x and X are coplanar
5
The epipolar geometry
What if only C,C,x are known?
6
The epipolar geometry
All points on p project on l and l
7
The epipolar geometry
Family of planes p and lines l and l
Intersection in e and e
8
The epipolar geometry
epipoles e,e intersection of baseline with
image plane projection of projection center in
other image vanishing point of camera motion
direction
an epipolar plane plane containing baseline
(1-D family)
an epipolar line intersection of epipolar plane
with image (always come in corresponding pairs)
9
Example converging cameras
10
Example motion parallel with image plane
(simple for stereo ? rectification)
11
Example forward motion
e
e
12
The fundamental matrix F
algebraic representation of epipolar geometry
we will see that mapping is (singular)
correlation (i.e. projective mapping from points
to lines) represented by the fundamental matrix F
13
The fundamental matrix F
geometric derivation
mapping from 2-D to 1-D family (rank 2)
14
The fundamental matrix F
algebraic derivation
(note doesnt work for CC ? F0)
15
The fundamental matrix F
correspondence condition
The fundamental matrix satisfies the condition
that for any pair of corresponding points x?x in
the two images
16
The fundamental matrix F
F is the unique 3x3 rank 2 matrix that satisfies
xTFx0 for all x?x
  1. Transpose if F is fundamental matrix for (P,P),
    then FT is fundamental matrix for (P,P)
  2. Epipolar lines lFx lFTx
  3. Epipoles on all epipolar lines, thus eTFx0, ?x
    ?eTF0, similarly Fe0
  4. F has 7 d.o.f. , i.e. 3x3-1(homogeneous)-1(rank2)
  5. F is a correlation, projective mapping from a
    point x to a line lFx (not a proper
    correlation, i.e. not invertible)

17
Fundamental matrix for pure translation
18
Fundamental matrix for pure translation
19
Fundamental matrix for pure translation
General motion
Pure translation
for pure translation F only has 2 degrees of
freedom
20
The fundamental matrix F
relation to homographies
valid for all plane homographies
21
The fundamental matrix F
relation to homographies
requires
22
Projective transformation and invariance
Derivation based purely on projective concepts
F invariant to transformations of projective
3-space
unique
not unique
canonical form
23
Projective ambiguity of cameras given F
previous slide at least projective
ambiguity this slide not more!
lemma
(22-157, ok)
24
Canonical cameras given F
25
Epipolar geometry?
courtesy Frank Dellaert
26
Triangulation
m1
C1
L1
Triangulation
  • calibration
  • correspondences

27
Triangulation
  • Backprojection
  • Triangulation

Iterative least-squares
  • Maximum Likelihood Triangulation

28
Backprojection
  • Represent point as intersection of row and column
  • Condition for solution?

Useful presentation for deriving and
understanding multiple view geometry (notice 3D
planes are linear in 2D point coordinates)
29
Next class computing F
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