faq.txt

做立体视觉时的标定算法

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Tsai Camera Calibration FAQ - Reg Willson
Date: Oct 27, 1995

If you have any questions, comments, or suggestions about this document,
the software, or camera calibration in general, please read item 5.

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 Table of contents
---------------------------
1 - What is Tsai's camera model?
2 - Where can I find the required intrinsic camera constants?
    2.1 - Why is the calibrated focal length not the same as the
          number printed on the lens?
3 - How do I collect calibration data?
    3.1 - Coplanar and non-coplanar data
    3.2 - Distribution of data points
    3.3 - Placement of world coordinate origin
    3.4 - Perspective projection effects (or lack thereof)
    3.5 - Subpixel measurement of image features
4 - What are some of the nonlinear optimization issues?
5 - Where can I find more information on Tsai's algorithm?
6 - Comments and thanks

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  Contents
---------------------------

1 - What is Tsai's camera model?

Tsai's camera model is based on the pin-hole model of 3D-2D perspective
projection with 1st order radial lens distortion. The model has 11
parameters: five internal (also called intrinsic or interior) parameters:

        f      - effective focal length of the pin-hole camera,
        kappa1 - 1st order radial lens distortion coefficient,
        Cx, Cy - coordinates of center of radial lens distortion -and-
                 the piercing point of the camera coordinate frame's
                 Z axis with the camera's sensor plane,
        sx     - scale factor to account for any uncertainty in the
                 framegrabber's resampling of the horizontal scanline.

and six external (also called extrinsic or exterior) parameters:

        Rx, Ry, Rz - rotation angles for the transform between the
                     world and camera coordinate frames,
        Tx, Ty, Tz - translational components for the transform between the
                     world and camera coordinate frames.

In addition to the 11 variable camera parameters Tsai's model has six fixed
intrinsic camera constants:

        Ncx - number of sensor elements in camera's x direction (in sels),
        Nfx - number of pixels in frame grabber's x direction (in pixels),
        dx  - X dimension of camera's sensor element (in mm/sel),
        dy  - Y dimension of camera's sensor element (in mm/sel),
        dpx - effective X dimension of pixel in frame grabber (in mm/pixel), and
        dpy - effective Y dimension of pixel in frame grabber (in mm/pixel).


2 - Where can I find the required intrinsic camera constants?

Commercial and industrial cameras often list Ncx, dx, and dy in their
user guides.  With consumer cameras you will generally need to contact
the manufacturer.

You don't need to know the actual values of the intrinsic constants to
calibrate an "accurate" camera model. For most applications "accurate"
simply means that given the 3D world coordinates of a point P the camera
model will accurately predict the 2D position of the point's image P' in
the frame buffer.  In these cases the true intrinsic constants aren't
necessary.  To get the camera calibration to converge to a solution you
only need the aspect ratio of the camera/frame grabber set up, i.e. the
ratio between dpx and dpy. You can get a good estimate for this from a
straight-on image of a rectangular target.  Measuring the rectangle, the
ratio

  (pixels from side-to-side in image) / (mm from side-to-side on target)
  ------------------------------------------------------------------------
  (pixels from top-to-bottom in image) / (mm from top-to-bottom on target)

should be an accurate enough estimate of dpx / dpy to allow the
calibration to converge.  The calibration algorithms (with the exception
of the basic Tsai coplanar calibration) will adjust the sx parameter to
automatically compensate for any error in the ratio of dpx / dpy.

Given the ratio of dpx to dpy, simply pick some value for dpy, say 10um (or
if you know it you can use the actual vertical pixel pitch) and use that to
back calculate dpx, dx, and dy. Set Ncx = Nfx, sx = 1.0, and Cx and Cy to
be the center of the frame buffer.  When you calibrate the model the
algorithm will adjust sx, Cx, and Cy to give a best fit set of intrinsic
parameters.

Observe if you double dpx and dpy, all the extrinsic parameters, Cx, Cy,
and sx, and the calibration error should remain the same.  The calibrated
camera model will be just as accurate.  The only thing that will change is
f and k1.


2.1 - Why is the calibrated focal length not the same as the number
      printed on the lens?

Even if you use the exact intrinsic camera parameters specified by the
manufacturer, the calibrated value of the effective focal length f is
unlikely to be the same as the focal length specified on the lens.  In
these calibration algorithms the effective focal length is a parameter
in a pin-hole camera model.  The focal length printed on the lens is a
parameter in a thick-lens camera model.  While the two parameters have
similar effects on the image they are actually quite different
quantities.


3 - How do I collect calibration data?

Calibration for the model consists of the 3D (x,y,z) world coordinates of a
feature point (in mm) and the corresponding coordinates (Xf,Yf) (in pixels)
of the feature point in the image.  The 3D coordinates must be specified in
a right-handed coordinate system.

Once a camera has been calibrated, subsequent calibrations at different
poses (i.e. with the camera rotated and/or translated) can be speeded up by
using the intrinsic parameters from the first calibration as a starting
point.


3.1 - Coplanar and non-coplanar data

Tsai's algorithm has two variants: one for coplanar data and one for
non-coplanar data.  For coplanar data Tsai's algorithm requires the z
component of the 3D coordinates to be 0.

Basic coplanar calibration requires at least five data points.  Basic
non-coplanar calibration requires at least seven data points.  Fully
optimized calibration requires at least 11 data points for either
coplanar and non-coplanar data.

The sx camera parameter cannot be calibrated for using coplanar data.  Its
value is left unchanged in the coplanar calibration routines.


3.2 - Distribution of data points

To accurately estimate the radial lens distortion and image center
parameters the calibration data should be distributed broadly across the
field of view.  The distribution of data points should, if possible, span
the range of depths that you expect to use the model over.


3.3 - Placement of world coordinate system origin

Tsai's algorithm fails if the origin of the world coordinate system is
near the center of the camera's field of view or near the Y axis of the
camera coordinate system.  The Y axis requirement ensures Ty is not
exactly zero which is an explicit requirement in Tsai's algorithm.

If your calibration data doesn't meet the above criteria you can simply
create a new, artificial coordinate frame for the data that is offset
from the world coordinate frame that you plan on working with.  Just add
the offset into the data before you calibrate with it.


3.4 - Perspective projection effects (or lack thereof)

To be able to separate the effects of f and Tz on the image there needs
to be perspective distortion (foreshortening) effects in the calibration
data.  For useable perspective distortion the distance between the
calibration points nearest and farthest from the camera should be on the
same scale as the distance between the calibration points and the
camera.  This applies both to coplanar and non-coplanar calibration.

For co-planar calibration the worst case situation is to have the 3D
points lie in a plane parallel to the camera's image plane (all points
an equal distance away).  Simple geometry tells us we can't separate the
effects of f and Tz.  A relative angle of 30 degrees or more is
recommended to give some effective depth to the data points.

For non-coplanar calibration the worse case situation is to have the 3D
points lie in a volume of space that is relatively small compared to the
volume's distance to the camera.  From a distance the image formation
process is closer to orthographic (not perspective) projection and the
calibration problem becomes poorly conditioned.


3.5 - Subpixel measurement of image features

To obtain accurate calibration data we need to measure the location of
features in the image plane to subpixel accuracy.  Three good starting
points on the subject are:

 "Accuracy in Image Measure", Pascal Brand and Roger Mohr, LIFIA - INRIA
 Rhones-Alpes, SPIE Vol. 2350 Videometrics III (1994) pages 218-228,

 "An evaluation of subpixel feature localisation methods for precision
 measurement", Robert J. Valkenburg, Alan M. McIvor, and P. Wayne Power,
 Machine Vision Group, Industrial Research Limited, SPIE Vol. 2350
 Videometrics III (1994) pages 229-238,

and

 "A comparison of some techniques for the subpixel location of discrete
 target images", M. R. Shortis, T. A. Clarke, and T. Short, Department of
 Surveying and Land Information, University of Melbourne, SPIE Vol. 2350
 Videometrics III (1994) pages 239-250.


4 - What are some of the nonlinear optimization issues?

Nonlinear optimization for these camera calibration routines is
performed by the MINPACK "lmdif" subroutine.  "lmdif" uses a modified
Levenberg-Marquardt algorithm with a jacobian calculated by a
forward-difference approximation.  The MINPACK FORTRAN source was
translated into C using the f2c program.

The function "dpmpar" provides double precision machine parameters for
the MINPACK functions.  The routines in this release are set up to use
the machine constants for the IEEE double precision floating point.  If
your machine uses a different floating point standard try looking in the
"dpmpar" source to see if the appropriate machine constants are
available.


5 - Where can I find more information on Tsai's algorithm?

An explanation of the basic algorithms and description of the variables
can be found in several publications, including:

 "An Efficient and Accurate Camera Calibration Technique for 3D Machine
 Vision", Roger Y. Tsai, Proceedings of IEEE Conference on Computer Vision
 and Pattern Recognition, Miami Beach, FL, 1986, pages 364-374.

and

 "A versatile Camera Calibration Technique for High-Accuracy 3D Machine
 Vision Metrology Using Off-the-Shelf TV Cameras and Lenses", Roger Y. Tsai,
 IEEE Journal of Robotics and Automation, Vol. RA-3, No. 4, August 1987,
 pages 323-344.


6 - Comments and thanks

Questions, comments, suggestions, or bug reports on Tsai's algorithm, this
implementation, or camera calibration in general can be mailed to:
[e-mail].  Special thanks to the following persons for
contributing their time, effort, and code!

 o Piotr Jasiobedzki <[e-mail]> 
 o Jim Vaughan <[e-mail]>
 o Franz-Josef L|cke <[e-mail]>
 o Pete Rander <[e-mail]>
 o Markus Menke <[e-mail]>
 o Ron Steriti <[e-mail]>
 o Torfi Thorhallsson <[e-mail]>
 o Frederic Devernay <[e-mail]>
 o Volker Rodehorst <[e-mail]>
 o Jon Owen <[e-mail]>

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