• DocumentCode
    2536780
  • Title

    Lens distortion calibration using point correspondences

  • Author

    Stein, G.P.

  • Author_Institution
    Artificial Intelligence Lab., MIT, Cambridge, MA, USA
  • fYear
    1997
  • fDate
    17-19 Jun 1997
  • Firstpage
    602
  • Lastpage
    608
  • Abstract
    This paper describes a new method for lens distortion calibration using only point correspondences in multiple views, without the need to know either the 3D location of the points or the camera locations. The standard lens distortion model is a model of the deviations of a real camera from the ideal pinhole or projective camera model. Given multiple views of a set of corresponding points taken by ideal pinhole cameras there exist epipolar and trilinear constraints among pairs and triplets of these views. In practice, due to noise in the feature detection and due to lens distortion these constraints do not hold exactly and we get some error. The calibration is a search for the lens distortion parameters that minimize this error. Using simulation and experimental results with real images we explore the properties of this method. We describe the use of this method with the standard lens distortion model, radial and decentering, but it could also be used with any other parametric distortion models. Finally we demonstrate that lens distortion calibration improves the accuracy of 3D reconstruction
  • Keywords
    calibration; cameras; computer vision; feature extraction; image reconstruction; 3D reconstruction; accuracy; calibration; constraints; feature detection; lens distortion calibration; multiple views; pinhole cameras; point correspondences; Artificial intelligence; Calibration; Cameras; Cost function; Image reconstruction; Least squares methods; Lenses; Nonlinear distortion; Nonlinear equations; Pixel;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on
  • Conference_Location
    San Juan
  • ISSN
    1063-6919
  • Print_ISBN
    0-8186-7822-4
  • Type

    conf

  • DOI
    10.1109/CVPR.1997.609387
  • Filename
    609387