Loading in 5 sec....

Camera models and calibrationPowerPoint Presentation

Camera models and calibration

- 107 Views
- Uploaded on
- Presentation posted in: General

Camera models and calibration

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Camera models and calibration

Read tutorial chapter 2 and 3.1

http://www.cs.unc.edu/~marc/tutorial/

Szeliski’s book pp.29-73

2D Ideal points

3D Ideal points

2D line at infinity

3D plane at infinity

2D line-point coincidence relation:

Point from lines:

Line from points:

3D plane-point coincidence relation:

Point from planes: Plane from points:

3D line representation:

(as two planes or two points)

l

Conics

x

C

l=Cx

Quadrics

Theorem:

A mapping h:P2P2is a projectivity if and only if there exist a non-singular 3x3 matrix H such that for any point in P2 reprented by a vector x it is true that h(x)=Hx

Definition: Projective transformation

or

8DOF

Definition:

A projectivity is an invertible mapping h from P2 to itself such that three points x1,x2,x3lie on the same line if and only if h(x1),h(x2),h(x3) do.

projectivity=collineation=projective transformation=homography

Transformation for conics

Transformation for dual conics

For a point transformation

Transformation for lines

(eigenvectors H-T =fixed lines)

(eigenvectors H =fixed points)

(1=2 pointwise fixed line)

transformed squares

invariants

Concurrency, collinearity, order of contact (intersection, tangency, inflection, etc.), cross ratio

Projective

8dof

Parallellism, ratio of areas, ratio of lengths on parallel lines (e.g midpoints), linear combinations of vectors (centroids).

The line at infinity l∞

Affine

6dof

Ratios of lengths, angles.

The circular points I,J

Similarity

4dof

Euclidean

3dof

lengths, areas.

The line at infinity l is a fixed line under a projective transformation H if and only if H is an affinity

Note: not fixed pointwise

projection

rectification

l∞

v1

v2

l1

l3

l2

l4

The circular points I, J are fixed points under the projective transformation H iff H is a similarity

l∞

Algebraically, encodes orthogonal directions

“circular points”

The dual conic is fixed conic under the

projective transformation H iff H is a similarity

Note: has 4DOF

l∞ is the nullvector

l∞

Projective:

(orthogonal)

Euclidean:

For a point transformation

(cfr. 2D equivalent)

Transformation for lines

Transformation for quadrics

Transformation for dual quadrics

Projective

15dof

Intersection and tangency

Parallellism of planes,

Volume ratios, centroids,

The plane at infinity π∞

Affine

12dof

Similarity

7dof

Angles, ratios of length

The absolute conic Ω∞

Euclidean

6dof

Volume

The plane at infinity π is a fixed plane under a projective transformation H iff H is an affinity

- canonical position
- contains directions
- two planes are parallel line of intersection in π∞
- line // line (or plane) point of intersection in π∞

The absolute conic Ω∞ is a (point) conic on π.

In a metric frame:

or conic for directions:

(with no real points)

The absolute conic Ω∞ is a fixed conic under the projective transformation H iff H is a similarity

- Ω∞is only fixed as a set
- Circle intersect Ω∞ in two circular points
- Spheres intersect π∞ in Ω∞

The absolute dual quadric Ω*∞ is a fixed conic under the projective transformation H iff H is a similarity

- 8 dof
- plane at infinity π∞ is the nullvector of Ω∞
- Angles:

Relation between pixels and rays in space

?

Pinhole camera

Gemma Frisius, 1544

- vanishing point

VPL

H

VPR

VP2

VP1

To different directions

correspond different vanishing points

VP3

- Points go to points
- Lines go to lines
- Planes go to whole image
or half-plane

- Polygons go to polygons
- Degenerate cases:
- line through focal point yields point
- plane through focal point yields line

Pinhole camera model

linear projection in homogeneous coordinates!

Pinhole camera model

Principal point offset

principal point

Principal point offset

calibration matrix

Camera rotation and translation

~

CCD camera

non-singular

General projective camera

11 dof (5+3+3)

intrinsic camera parameters

extrinsic camera parameters

- Due to spherical lenses (cheap)
- Model:

R

R

straight lines are not straight anymore

http://foto.hut.fi/opetus/260/luennot/11/atkinson_6-11_radial_distortion_zoom_lenses.jpg

Relation between pixels and rays in space

?

Relation between pixels and rays in space

(dual of camera)

(main geometric difference is vertical principal point offset to reduce keystone effect)

?

vertical lens shift

to allow direct

ortho-photographs

Affine cameras

Action of projective camera on points and lines

projection of point

forward projection of line

back-projection of line

Action of projective camera on conics and quadrics

back-projection to cone

projection of quadric

Resectioning

Direct Linear Transform (DLT)

rank-2 matrix

minimize subject to constraint

Direct Linear Transform (DLT)

Minimal solution

P has 11 dof, 2 independent eq./points

- 5½ correspondences needed (say 6)

Over-determined solution

n 6 points

use SVD

Degenerate configurations

- Points lie on plane or single line passing through projection center
- Camera and points on a twisted cubic

Data normalization

- Scale data to values of order 1
- move center of mass to origin
- scale to yield order 1 values

Line correspondences

Extend DLT to lines

(back-project line)

(2 independent eq.)

Geometric error

- Objective
- Given n≥6 2D to 3D point correspondences {Xi↔xi’}, determine the Maximum Likelyhood Estimation of P
- Algorithm
- Linear solution:
- Normalization:
- DLT

- Minimization of geometric error: using the linear estimate as a starting point minimize the geometric error:
- Denormalization:

~

~

~

Calibration example

- Canny edge detection
- Straight line fitting to the detected edges
- Intersecting the lines to obtain the images corners
- typically precision <1/10
- (H&Z rule of thumb: 5n constraints for n unknowns)

Errors in the image

(standard case)

Errors in the world

Errors in the image and in the world

Restricted camera estimation

- Find best fit that satisfies
- skew s is zero
- pixels are square
- principal point is known
- complete camera matrix K is known

- Minimize geometric error
- impose constraint through parametrization

- Minimize algebraic error
- assume map from param q P=K[R|-RC], i.e. p=g(q)
- minimize ||Ag(q)||

Restricted camera estimation

- Initialization
- Use general DLT
- Clamp values to desired values, e.g. s=0, x= y
- Note: can sometimes cause big jump in error
- Alternative initialization
- Use general DLT
- Impose soft constraints
- gradually increase weights

A simple calibration device

- compute H for each square
- (corners (0,0),(1,0),(0,1),(1,1))
- compute the imaged circular points H(1,±i,0)T
- fit a conic to 6 circular points
- compute K from w through cholesky factorization

(≈ Zhang’s calibration method)

Some typical calibration algorithms

Tsai calibration

Zhangs calibration

http://research.microsoft.com/~zhang/calib/

Z. Zhang. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334, 2000.

Z. Zhang. Flexible Camera Calibration By Viewing a Plane From Unknown Orientations. International Conference on Computer Vision (ICCV'99), Corfu, Greece, pages 666-673, September 1999.

http://www.vision.caltech.edu/bouguetj/calib_doc/

from Szeliski’s book