more on single-view geometry class 10
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More on single-view geometry class 10. Multiple View Geometry Comp 290-089 Marc Pollefeys. Multiple View Geometry course schedule (subject to change). Single view geometry. Camera model Camera calibration Single view geom. Gold Standard algorithm. Objective - PowerPoint PPT PresentationTRANSCRIPT
More on single-view geometry
class 10
Multiple View GeometryComp 290-089Marc Pollefeys
Multiple View Geometry course schedule(subject to change)
Jan. 7, 9 Intro & motivation Projective 2D Geometry
Jan. 14, 16
(no class) Projective 2D Geometry
Jan. 21, 23
Projective 3D Geometry (no class)
Jan. 28, 30
Parameter Estimation Parameter Estimation
Feb. 4, 6 Algorithm Evaluation Camera ModelsFeb. 11, 13
Camera Calibration Single View Geometry
Feb. 18, 20
Epipolar Geometry 3D reconstruction
Feb. 25, 27
Fund. Matrix Comp. Structure Comp.
Mar. 4, 6 Planes & Homographies Trifocal TensorMar. 18, 20
Three View Reconstruction
Multiple View Geometry
Mar. 25, 27
MultipleView Reconstruction
Bundle adjustment
Apr. 1, 3 Auto-Calibration PapersApr. 8, 10
Dynamic SfM Papers
Apr. 15, 17
Cheirality Papers
Apr. 22, 24
Duality Project Demos
Single view geometry
Camera model
Camera calibration
Single view geom.
Gold Standard algorithmObjective
Given n≥6 2D to 2D point correspondences {Xi↔xi’}, determine the Maximum Likelyhood Estimation of P
Algorithm(i) Linear solution:
(a) Normalization: (b) DLT
(ii) Minimization of geometric error: using the linear estimate as a starting point minimize the geometric error:
(iii) Denormalization:
ii UXX~ ii Txx~
UP~TP -1
~ ~~
More Single-View Geometry
• Projective cameras and planes, lines, conics and quadrics.
• Camera calibration and vanishing points, calibrating conic and the IAC
** CPPQ T
coneQCPP T
Action of projective camera on planes
1ppp
10ppppPXx 4214321 Y
XYX
The most general transformation that can occur between a scene plane and an image plane under perspective imaging is a plane projective transformation
(affine camera-affine transformation)
Action of projective camera on lines
forward projection
μbaμPBPAμB)P(AμX
back-projection
lPT
PXlX TT
Action of projective camera on conics
back-projection to cone
CPPQ Tco 0CPXPXCxx TTT
00
0CKK0|KC0KQ
TT
T
co
example:
Images of smooth surfaces
The contour generator is the set of points X on S at which rays are tangent to the surface. The corresponding apparent contour is the set of points x which are the image of X, i.e. is the image of
The contour generator depends only on position of projection center, depends also on rest of P
Action of projective camera on quadrics
back-projection to cone
TPPQC ** 0lPPQlQ T*T*T
The plane of for a quadric Q is camera center C is given by =QC (follows from pole-polar relation)The cone with vertex V and tangent to the quadric Q is
TTCO (QV)(QV)-QV)QV(Q 0VQCO
The importance of the camera center
]C~|[IR'K'P'C],|KR[IP
PKRR'K'P' -1
xKRR'K'PXKRR'K'XP'x' -1-1
-1KRR'K'HHx with x'
Moving the image plane (zooming)
xKK'0]X|[IK'x'0]X|K[Ix
1-
10
x~k)(1kIKK'H T01-
100kIK
10x~kA
10x~A
10x~k)(1kIK
10x~k)(1kIK'
TT0
T0
T0
T0
'/ ffk
Camera rotation
xKRK0]X|K[Rx'0]X|K[Ix
1-
-1KRKH
conjugate rotation
ii ee μ,μμ,
Synthetic view
(i) Compute the homography that warps some a rectangle to the correct aspect ratio
(ii) warp the image
Planar homography mosaicing
close-up: interlacingcan be important problem!
Planar homography mosaicingmore examples
Projective (reduced) notation
T4
T3
T2
T1 )1,0,0,0(X,)0,1,0,0(X,)0,0,1,0(X,)0,0,0,1(X
T4
T3
T2
T1 )1,1,1(x,)1,0,0(x,)0,1,0(x,)0,0,1(x
dcdbda
000000
P
Tdcba ),,,(C 1111
Moving the camera center
motion parallax
epipolar line
What does calibration give?
xKd 1
0d0]|K[Ix
21-T-T
211-T-T
1
2-1-TT
1
2T
21T
1
2T
1
)xK(Kx)xK(Kx
)xK(Kx
dddd
ddcos
An image l defines a plane through the camera center with normal n=KTl measured in the camera’s Euclidean frame
The image of the absolute conic
KRd0d]C~|KR[IPXx
mapping between ∞ to an image is given by the planar homogaphy x=Hd, with H=KR
image of the absolute conic (IAC)
1-T-1T KKKKω 1TCHHC
(i) IAC depends only on intrinsics(ii) angle between two rays(iii) DIAC=*=KKT
(iv) K (cholesky factorisation)(v) image of circular points
2T
21T
1
2T
1
ωxxωxx
ωxxcos
A simple calibration device
(i) compute H for each square (corners (0,0),(1,0),(0,1),(1,1))
(ii) compute the imaged circular points H(1,±i,0)T
(iii) fit a conic to 6 circular points(iv) compute K from through cholesky factorization
(= Zhang’s calibration method)
Orthogonality = pole-polar w.r.t. IAC
The calibrating conic
1T K1
11
KC
Vanishing points
λKdaλPDPAλPXλx
KdλKda limλ xlimvλλ
KdPXv
Vanishing lines
Orthogonality relation
2T
21T
1
2T
1
ωvvωvv
ωvvcos
0ωvv 2T
1
0lωl 2*T
1
Calibration from vanishing points and lines
Calibration from vanishing points and lines
Next class: Two-view geometryEpipolar geometry