lecture 03 15/11/2011 shai avidan הבהרה : החומר המחייב הוא החומר הנלמד...
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Lecture 03
15/11/2011Shai Avidan
הבהרה: מופיע / לא המופיע זה ולא בכיתה הנלמד החומר הוא המחייב החומר
במצגת.
• Perspective Projection• Direct Linear Transformation• Traiangulation
PERSPECTIVE PROJECTION
Perspective projection
Scene point Image coordinates
Camera frame
Image plane
Optical axis
Focal length
Thus far, in camera’s reference frame only.
• Extrinsic: location and orientation of camera frame with respect to reference frame
• Intrinsic: how to map pixel coordinates to image plane coordinates
Camera parameters
Camera 1 frame
Reference frame
Extrinsic camera parameters
)( TPRP wc
World reference frame
Camera reference frame
Tc ZYX ,,P
• Extrinsic: location and orientation of camera frame with respect to reference frame
• Intrinsic: how to map pixel coordinates to image plane coordinates
Camera parameters
Camera 1 frame
Reference frame
Intrinsic camera parameters
•Ignoring any geometric distortions from optics, we can describe them by:
xxim soxx )(
yyim soyy )(
Coordinates of projected point in camera reference
frame
Coordinates of image point in
pixel units
Coordinates of image center in
pixel units
Effective size of a pixel (mm)
Camera parameters• We know that in terms of camera reference frame:
• Substituting previous eqns describing intrinsic and extrinsic parameters, can relate pixels coordinates to world points:
)(
)()(
3
1
TPR
TPR
w
wxxim fsox
)(
)()(
3
2
TPR
TPR
w
wyyim fsoy
Ri = Row i of rotation matrix
)( TPRP wcand Tc ZYX ,,P
Projection matrix
• This can be rewritten as a matrix product using homogeneous coordinates:
• The motion of the camera is equivalent to the inverse motion of the scene
wxim
wyim
w
= K[R t]Xw
Yw
Zw
1
100/00/
yy
xx
osfosf
K=
wwyy
wwxx
imim
imim
/
/
point in camera coordinates
10
RTR
External parametersInternal parameters
Calibrating a camera• Compute intrinsic and extrinsic
parameters using observed camera data
• Main idea• Place “calibration object” with
known geometry in the scene• Get correspondences• Solve for mapping from scene to
image: estimate M=K[R t]
• This can be rewritten as a matrix product using homogeneous coordinates:
= K[R t]Xw
Yw
Zw
1
w
wim
w
wim
y
x
PM
PM
PM
PM
3
2
3
1
M
Let Mi be row i of matrix M
product M is single projection matrix encoding both extrinsic and intrinsic parameters
Pw in homog.
Projection matrix
wxim
wyim
w
• This can be rewritten as a matrix product using homogeneous coordinates:
= K[R t]Xw
Yw
Zw
1M
Let Mi be row i of matrix M
product M is single projection matrix encoding both extrinsic and intrinsic parameters
wwyy
wwxx
imim
imim
/
/
Pw in homog.
Projection matrix
wxim
wyim
w
Estimating the projection matrix
w
wim
w
wim
y
x
PM
PM
PM
PM
3
2
3
1wimx PMM )(0 31
wimy PMM )(0 32
For a given feature point
Estimating the projection matrixwimx PMM )(0 31
wimy PMM )(0 32
0
1
*3433323114131211
w
w
w
imZ
Y
X
mmmmxmmmm
Expanding this first equation, we have:
...32123111 mxYmYmxXmX imwwimww
0... 34143313 mxmmxZmZ imimww
Estimating the projection matrix
imwimwimwimwww
imwimwimwimwww
yZyYyXyZYXxZxYxXxZYX
1000000001
00
wimx PMM )(0 31
wimy PMM )(0 32
Estimating the projection matrixThis is true for every feature point, so we can stack up n observed image features and their associated 3d points in single equation:
Solve for mij’s (the calibration information) [F&P Section 3.1]
)1()1()1()1()1()1()1()1()1()1(
)1()1()1()1()1()1()1()1()1()1(
10000
00001
imwimwimwwww
imwimwimwimwww
yZyYyXyZYX
xZxYxXxZYX
im
… … … …
)()()()()()()()()()(
)()()()()()()()()()(
10000
00001nnnnnnn
imnnn
nnnnnnnnnn
imwimwimwwww
imwimwimwimwww
yZyYyXyZYX
xZxYxXxZYX
00…
00
P m
Pm = 0
Summary: camera calibration
• Associate image points with scene points on object with known geometry
• Use together with perspective projection relationship to estimate projection matrix
• (Can also solve for explicit parameters themselves)
DIRECT LINEAR TRANSFORMATION
Direct Linear Transformation from 3D to 2D
• Given two cameras (a stereo pair) and a 3D object with known 3D coordinates we can:
1) For each camera compute the direct linear transformation from 3D to 2D
• Now we can move the stereo pair to a different place, take a pair of pictures and recover 3D information:
1) Given two camera matrices and a pair of matching points, we can triangulate to get its depth.
2) We’ll talk about triangulation later in class
When would we calibrate this way?
• Makes sense when geometry of system is not going to change over time
• …When would it change?
TRIANGULATION
24
Triangulation
• Problem: Given some points in correspondence across two or more images (taken from calibrated cameras), {(uj,vj)}, compute the 3D location X
25
Triangulation
• Method I: intersect viewing rays in 3D, minimize:
– X is the unknown 3D point– Cj is the optical center of camera j– Vj is the viewing ray for pixel (uj,vj)
– sj is unknown distance along Vj
• Advantage: geometrically intuitive
Cj
Vj
X
26
Triangulation• Method II: solve linear equations in X
– advantage: very simple
• Method III: non-linear minimization
– advantage: most accurate (image plane error)