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A Flexible New Technique for Camera Calibration
November 2000 (vol. 22 no. 11)
pp. 1330-1334

Abstract—We propose a flexible new technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one more step from laboratory environments to real world use. The corresponding software is available from the author's Web page.

[1] S. Bougnoux, “From Projective to Euclidean Space under any Practical Situation, a Criticism of Self-Calibration,” Proc. Sixth Int'l Conf. Computer Vision, pp. 790-796, Jan. 1998.
[2] D.C. Brown, “Close-Range Camera Calibration,” Photogrammetric Eng., vol. 37, no. 8, pp. 855-866, 1971.
[3] B. Caprile and V. Torre, “Using Vanishing Points for Camera Calibration,” Int'l J. Computer Vision, vol. 4, no. 2, pp. 127-140, Mar. 1990.
[4] W. Faig, “Calibration of Close-Range Photogrammetry Systems: Mathematical Formulation,” Photogrammetric Eng. and Remote Sensing, vol. 41, no. 12, pp. 1,479-1,486, 1975.
[5] O.D. Faugeras, Three-Dimensional Computer Vision: A Geometric Viewpoint.Cambridge, Mass.: MIT Press, 1993.
[6] O. Faugeras, T. Luong, and S. Maybank, “Camera Self-Calibration: Theory and Experiments,” Proc Second European Conf. Computer Vision, pp. 321-334, May 1992.
[7] O. Faugeras and G. Toscani, “The Calibration Problem for Stereo,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 15-20, June 1986.
[8] S. Ganapathy, “Decomposition of Transformation Matrices for Robot Vision,” Pattern Recognition Letters, vol. 2, pp. 401-412, Dec. 1984.
[9] D. Gennery, “Stereo-Camera Calibration,” Proc. 10th Image Understanding Workshop, pp. 101-108, 1979.
[10] G. Golub and C. van Loan, Matrix Computations, Baltimore: The John Hopkins Univ. Press, third ed. 1996.
[11] R. Hartley, “Self-Calibration from Multiple Views with a Rotating Camera,” Proc. Third European Conf. Computer Vision, pp. 471-478, May 1994.
[12] R.I. Hartley, “An Algorithm for Self-Calibration from Several Views,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 908-912, June 1994.
[13] D. Liebowitz and A. Zisserman, “Metric Rectification for Perspective Images of Planes,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 482-488, June 1998.
[14] Q.-T. Luong, “Matrice Fondamentale et Calibration Visuelle sur l'Environnement-Vers une plus Grande Autonomie des Systèmes Robotiques,” PhD thesis, Universitéde Paris-Sud, Centre d'Orsay, Dec. 1992.
[15] Q.-T. Luong and O. Faugeras, “Camera Calibration, Scene Motion and Structure Recovery from Point Correspondences and Fundamental Matrices,” Int'l J. Computer Vision, vol. 22, no. 3, pp. 261-289, 1997.
[16] S.J. Maybank and O.D. Faugeras, “A Theory of Self-Calibration of a Moving Camera,” Int'l J. Computer Vision, vol. 8. no. 2, pp. 123-152, Aug. 1992.
[17] J. More, “The Levenberg-Marquardt Algorithm, Implementation, and Theory,” Numerical Analysis, G.A. Watson, ed., Springer-Verlag, 1977.
[18] J. Semple and G. Kneebone, Algebraic Projective Geometry. Oxford: Clarendon Press, 1952.
[19] I. Shimizu, Z. Zhang, S. Akamatsu, and K. Deguchi, “Head Pose Determination from One Image Using a Generic Model,” Proc. IEEE Third Int'l Conf. Automatic Face and Gesture Recognition, pp. 100-105, Apr. 1998.
[20] G. Stein, “Accurate Internal Camera Calibration Using Rotation, with Analysis of Sources of Error,” Proc. Fifth Int'l Conf. Computer Vision, pp. 230-236, June 1995.
[21] P. Sturm and S. Maybank, “On Plane-Based Camera Calibration: A General Algorithm, Singularities, Applications,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 432-437, June 1999.
[22] B. Triggs, “Autocalibration from Planar Scenes,” Proc. Fifth European Conf. Computer Vision, pp. 89-105, June 1998.
[23] R. Tsai, "A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses," IEEE J. Robotics and Automation, vol. 3, no. 4, pp. 323-344, Aug. 1987.
[24] G. Wei and S. Ma, “A Complete Two-Plane Camera Calibration Method and Experimental Comparisons,” Proc. Fourth Int'l Conf. Computer Vision, pp. 439-446, May 1993.
[25] J. Weng, P. Cohen, and M. Herniou, “Camera Calibration with Distortion Models and Accuracy Evaluation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 10, pp. 965-980, Oct. 1992.
[26] Z. Zhang, “A Flexible New Technique for Camera Calibration,” Technical Report MSR-TR-98-71, Microsoft Research, Dec. 1998. Available together with the software athttp://research.microsoft.com/~zhangCalib /.

Index Terms:
Camera calibration, calibration from planes, 2D pattern, flexible plane-based calibration, absolute conic, projective mapping, lens distortion, closed-form solution, maximum likelihood estimation, flexible setup.
Citation:
Zhengyou Zhang, "A Flexible New Technique for Camera Calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330-1334, Nov. 2000, doi:10.1109/34.888718
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