A Minimal Solution for Camera Calibration Using Independent Pairwise Correspondences

  • Francisco Vasconcelos
  • João Pedro Barreto
  • Edmond Boyer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

We propose a minimal algorithm for fully calibrating a camera from 11 independent pairwise point correspondences with two other calibrated cameras. Unlike previous approaches, our method neither requires triple correspondences, nor prior knowledge about the viewed scene. This algorithm can be used to insert or re-calibrate a new camera into an existing network, without having to interrupt operation. Its main strength comes from the fact that it is often difficult to find triple correspondences in a camera network. This makes our algorithm, for the specified use cases, probably the most suited calibration solution that does not require a calibration target, and hence can be performed without human interaction.

Keywords

Camera Networks Multiple View Geometry Camera Calibration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Barreto, J., Daniilidis, K.: Wide area multiple camera calibration and estimation of radial distortion. In: OMNIVIS 2004 - Int. Workshop in Omnidirectional Vision, Camera Networks, and Non-conventional Cameras (2004)Google Scholar
  3. 3.
    Barreto, J.P.: General central projection systems: Modeling, calibration and visual servoing. Ph.D. thesis, University of Coimbra, Coimbra, Portugal (2004)Google Scholar
  4. 4.
    Courchay, J., Dalalyan, A., Keriven, R., Sturm, P.: A global camera network calibration method with linear programming. In: Proceedings of the International Symposium on 3D Data Processing, Visualization and Transmission (2010)Google Scholar
  5. 5.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hartley, R., Sturm, P.: Triangulation. Computer Vision and Image Understanding (1997)Google Scholar
  7. 7.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge Academic Press (2003)Google Scholar
  8. 8.
    Josephson, K., Byrod, M., Kahl, F., Åström, K.: Image-based localization using hybrid feature correspondences. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (2007)Google Scholar
  9. 9.
    Kim, J., Hodong, L., Hartley, R.: Motion Estimation for Nonoverlapping Multicamera Rigs: Linear Algebraic and Linf Geometric Solutions. IEEE Trans. in Pattern Analysis and Machine Intelligence 32(6), 1044–1058 (2010)CrossRefGoogle Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Ma, Y., Soatto, S., Kosecka, J., Sastry, S.: An invitation to 3-D vision: from images to geometric models. Springer (2004)Google Scholar
  12. 12.
    Pless, R.: Using many cameras as one. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2003)Google Scholar
  13. 13.
    Pottmann, H., Wallner, J.: Computational line geometry, 1st edn. Springer, Berlin (2001)MATHGoogle Scholar
  14. 14.
    Shen, E., Hornsey, R.: Multi-camera network calibration with a non-planar target. IEEE Sensors Journal 11(10), 2356–2364 (2011)Google Scholar
  15. 15.
    Starck, J., Hilton, A.: Surface capture for performance-based animation. IEEE Computer Graphics and Applications 27(3), 21–31 (2007)CrossRefGoogle Scholar
  16. 16.
    Stewénius, H., Nistér, D., Oskarsson, M., Åström, K.: Solutions to minimal generalized relative pose problems. In: Workshop on Omnidirectional Vision, Beijing, China (2005)Google Scholar
  17. 17.
    Sturm, P., Triggs, B.: A Factorization Based Algorithm for Multi Image Projective Structure and Motion. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 709–720. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  18. 18.
    Svoboda, T., Martinec, D., Pajdla, T.: A convenient multicamera self-calibration for virtual environments. Presence: Teleoper. Virtual Environ. 14(4), 407–422 (2005)CrossRefGoogle Scholar
  19. 19.
    Zaharescu, A., Horaud, R., Ronfard, R., Lefort, L.: Multiple camera calibration using robust perspective factorization. In: Third International Symposium on 3D Data Processing, Visualization, and Transmission, pp. 504–511 (2006)Google Scholar
  20. 20.
    Zhao, Z., Liu, Y.: Practical multi-camera calibration algorithm with 1D objects for virtual environments. In: 2008 IEEE International Conference on Multimedia and Expo., pp. 1197–1200 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francisco Vasconcelos
    • 1
  • João Pedro Barreto
    • 1
  • Edmond Boyer
    • 2
  1. 1.ISR, University of CoimbraPortugal
  2. 2.INRIA Grenoble Rhône-AlpesFrance

Personalised recommendations