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IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quadratic Eigenvalue Problem
SIAM Review
Lens distortion calibration using point correspondences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Rational Function Lens Distortion Model for General Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Radial Trifocal Tensor: A Tool for Calibrating the Radial Distortion of Wide-Angle Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Generalized Eigenvalue Problem for Nonsquare Pencils Using a Minimal Perturbation Approach
SIAM Journal on Matrix Analysis and Applications
Ultra displays and the challenge of unlimited resolution
EDT '07 Proceedings of the 2007 workshop on Emerging displays technologies: images and beyond: the future of displays and interacton
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
Camera Models and Fundamental Concepts Used in Geometric Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Distortion compensation for movement detection based on dense optical flow
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Self-calibration of wireless cameras with restricted degrees of freedom
Computer Vision and Image Understanding
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This paper introduces a new method for simultaneous estimation of lens distortion and multi-view geometry using only point correspondences. The new technique has significant advantages over the current state-of-the art in that it makes more effective use of correspondences arising from any number of views. Multi-view geometry in the presence of lens distortion can be expressed as a set of point correspondence constraints that are quadratic in the unknown distortion parameter. Previous work has demonstrated how the system can be solved efficiently as a quadratic eigenvalue problem by operating on the normal equations of the system. Although this approach is appropriate for situations in which only a minimal set of matchpoints are available, it does not take full advantage of extra correspondences in overconstrained situations, resulting in significant bias and many potential solutions. The new technique directly operates on the initial constraint equations and solves the quadratic eigenvalue problem in the case of rectangular matrices. The method is shown to contain significantly less bias on both controlled and real-world data and, in the case of a moving camera where additional views serve to constrain the number of solutions, an accurate estimate of both geometry and distortion is achieved.