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Camera Calibration and Reconstruction
of Geometry from Images
David Liebowitz
Merton College
Robotics Research Group
Department of Engineering Science
University of Oxford
Trinity Term 2001
David Liebowitz
Merton College
Doctor of Philosophy
Trinity Term 2001
Abstract
This thesis addresses the issues of combining camera calibration constraints from various sources
and reconstructing scene geometry from single and multiple views. A geometric approach is taken,
associating both structure recovery and calibration with geometric entities.
Three sources of calibration constraints are considered: scene constraints, such as the paral-
lelism and orthogonality of lines, constraints from partial knowledge of camera parameters, and
constraints derived from the motion between views.
First, methods of rectifying the projective distortion in an imaged plane are examined. Metric
rectification constraints are developed by constraining the imaged plane circular points.
The internal camera parameters are associated with the absolute conic. It is shown how imaged
plane circular points constrain the image of the absolute conic, and are constrained by a known
absolute conic in return. A method of using planes with known metric structure as a calibration
object is developed.
Next, calibration and reconstruction from single views is addressed. A well known configuration
of the vanishing points of three orthogonal directions and knowledge that the camera has square
pixels is expressed geometrically and subjected to degeneracy and error analysis. The square pixel
constraint is shown to be geometrically equivalent to treating the image plane as a metric scene
plane.
Use of the vanishing point configuration is extended to two views, where three vanishing points
and known epipolar geometry define a three dimensional affine reconstruction. Calibration and
metric reconstruction follows similarly to the single view case, with the addition of auto-calibration
constraints from the motion between views. The auto-calibration constraints are derived from the
geometric representation of the square pixel constraints, by transferring the image plane circular
points between views. Degenerate cases for constraints from square pixels and cameras having
identical internal parameters are described.
Finally, a constraint on the metric rectification of an affine reconstruction from the relative
lengths of a pair of 3D line segments is developed. The constraint is applied to human motion
capture from a pair of affine cameras.
Dedication
For my father, Frederick Liebowitz.
Acknowledgements
My years in Oxford have been an apprenticeship under a master of the craft, Andrew Zisserman. His
insight and guidance have shaped this thesis, and I remain deeply grateful for all that he has taught
me. Andrew Fitzgibbon has been a great source of help in all software matters and also patiently
explained a great deal of vision to me. Most of all I would like to thank him for his boundless and
infectious enthusiasm. Most of the last year was spent as a guest at the Royal Institute of Technology
in Stockholm. I would like to thank Stefan Carlsson for making this a thoroughly rewarding and
enjoyable experience.
I learned a great deal from my colleagues, and friends, in the Visual Geometry Group. Fred Schaf-
falitzky, Phil Pritchett, Antonio Criminisi, David Capel, Geoff Cross and Nic Pillow created a lively
environment for research that I continue to miss. I would also like to thank Eric Hayman, Matthew
Bryant and Sebastien Rougeaux for proofreading parts of this thesis.
Contents
1 Introduction 1
1.1 An overview ...................................... 2
1.2 Thinking in pictures .................................. 6
1.3 Thesis structure . . .................................. 7
2 Background 9
2.1 Introduction ...................................... 9
2.2 Projective spaces and transformations . ....................... 10
2.2.1 The projective line . ............................. 10
2.2.2 The projective plane ............................. 11
2.2.3 The three dimensional projective space . .................. 17
2.3 Cameras and reconstruction . ............................. 21
2.3.1 The pinhole camera . ............................. 22
2.3.2 Epipolar geometry and reconstruction from uncalibrated cameras ..... 23
2.3.3 Stratified metric rectification . . ....................... 25
2.3.4 Specialization in reconstruction ....................... 26
2.3.5 The image of the absolute conic ....................... 27
2.3.6 Rectified camera matrices and the infinite homography ........... 29
2.4 Calibration and auto-calibration literature ...................... 30
2.4.1 Classical calibration ............................. 31
2.4.2 Auto-calibration with constant internal parameters . ............ 31
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