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Multiple View Geometry

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Multiple View Geometry – PowerPoint PPT presentation

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Title: Multiple View Geometry


1
Multiple View Geometry
  • Jim Rehg
  • CS 4495/7495
  • Many slides by Frank Dellaert
  • (reconstruction by Antonio Criminsi)

2
Book Sequence
3
Book Structure Motion
4
2 Problems !
  • Correspondence
  • Optimization

5
A Correspondence Problem
6
An Optimization Problem
  • Find the most likely structure and motion ?

7
Bundle Adjustment
Image i
Image i
uik
8
Geometric Intuition !!
  • From first lecture on cameras !

(0.6,2.5,1) ?
Z
Y
X
9
Geometric Intuition
  • Projection matrix form


Z
Y
X
10
Affine Cameras
  • Easy projection equation

11
Translation of image i ?
  • Take derivative wrpt to ti

12
World origin trick
  • Move scene so centroid origin

13
New Problem !
  • Just subtract centroid from all measurements

14
Matrix Form
  • Projection in one image (i)

15
Matrix Form
  • Projection in two images

16
Matrix Form
  • Projection all points in all images

n
16
13
14
M1
1
15
2
3
11
12
4
5
6

24
M2
25
26
21
22
23
2m
34
M3
35
36
31
32
33
44
M4
45
46
41
42
43
17
Geometric Intuition !!
  • Joint Image of point 8-dimensional

Z
Y
X
18
Subspace Constraints
  • Does it really span 8-dim space ??

Z
Y
X
19
Joint Camera Mapping
83
R3
R8
Mapping from 3D to 8D space (for 4 cameras)
20
But Range is only 3D !!!
83
R3
R3
R5
5-dimensional null-space !
21
What does SVD look like ?
AU S VT
3x3
83
3x3
R3
R3P2
R5
Rank 3, economy SVD
22
And measurement matrix ?
Also Rank 3 !!!
23
SVD Approach to SFM
  • Form measurement matrix
  • Do rank 3 economy size SVD
  • Voila

Motion
Structure
24
Problem
  • Where to stick the extra 3x3 ?

Motion
Structure
3x3
3x3
83

25
Euclidean Upgrade
  • Uncalibrated not so easy
  • Calibrated cameras
  • We know they are rotation matrices
  • Impose constraints
  • unit length 2m
  • orthogonal m
  • Solve for Q

3x3
3x3
26
Additional Details
  • How to obtain measurements?
  • Tracking of point features across video
  • (We will discuss this later)
  • Two-step solution process in general
  • Initial solution
  • Factorization, 2-view or 3-view solutions
  • Bundle adjustment
  • Nonlinear minimization from good starting point

27
UrbanScape
  • Large scale 3D modeling from video
  • Integrated system combining recent techniques
  • Large scale 500K frames
  • Uses GPU acceleration
  • DARPA funded project, PIs
  • Marc Pollefeys (UNC Chapel Hill, now ETH)
  • David Nister (Univ. Kentucky, now MSR)
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