A complete two-plane camera calibration method and experimental comparisons

For both 3-D reconstruction and prediction of image coordinates, cameras can be calibrated implicitly without involving their physical parameters. The authors present a two-plane method for such a complete calibration, which models all kinds of lens distortions. First, the modeling is done in a general case without imposing the pinhole constraint. Epipolar curves considering lens distortions are introduced and are found in a closed form. Then, a set of constraints of perspectivity is derived to constrain the modeling process. With these constraints, the camera physical parameters can be related directly to the modeling parameters. Extensive experimental comparisons of the methods with the classic photogrammetric method and Tsai's method relating to the aspects of 3-D measurement, the effect of the number of calibration points, and the prediction of image coordinates, are made using real images from 15 different depth values.<<ETX>>

[1]  Louis A. Tamburino,et al.  A Unified Approach to the Linear Camera Calibration Problem , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Songde Ma,et al.  Two plane camera calibration: a unified model , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Yoram Yakimovsky,et al.  A system for extracting three-dimensional measurements from a stereo pair of TV cameras , 1976 .

[4]  Robert B. Kelley,et al.  Camera Models Based on Data from Two Calibration Planes , 1981 .

[5]  Pearl Pu,et al.  A new development in camera calibration calibrating a pair of mobile cameras , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[6]  Luce Morin,et al.  Relative positioning from geometric invariants , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Juyang Weng,et al.  Calibration of stereo cameras using a non-linear distortion model (CCD sensory) , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[8]  Michael A. Penna Camera Calibration: A Quick and Easy Way to Determine the Scale Factor , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  K W Wong MATHEMATICAL FORMULATION AND DIGITAL ANALYSIS IN CLOSE-RANGE PHOTOGRAMMETRY , 1975 .

[10]  Olivier D. Faugeras,et al.  What can be seen in three dimensions with an uncalibrated stereo rig , 1992, ECCV.

[11]  W. Faig CALIBRATION OF CLOSE-RANGE PHOTOGRAMMETRIC SYSTEMS: MATHEMATICAL FORMULATION , 1975 .

[12]  R. Y. Tsai,et al.  An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision , 1986, CVPR 1986.

[13]  Roger Y. Tsai,et al.  Techniques for calibration of the scale factor and image center for high accuracy 3D machine vision metrology , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[14]  Songde Ma,et al.  Implicit and Explicit Camera Calibration: Theory and Experiments , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Roger Y. Tsai,et al.  Techniques for Calibration of the Scale Factor and Image Center for High Accuracy 3-D Machine Vision Metrology , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Takeo Kanade,et al.  Geometric camera calibration using systems of linear equations , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[17]  Sundaram Ganapathy,et al.  Decomposition of transformation matrices for robot vision , 1984, Pattern Recognit. Lett..