Calibration of Shared Flat Refractive Stereo Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)

Abstract

The calibration of underwater camera systems differs significantly from calibration in air due to the refraction of light. In this paper, we present a calibration approach for a shared flat refractive stereo system that is based on virtual object points. We propose a sampling strategy in combination with an efficiently solvable set of equations for the calibration of the refractive parameters. Due to the independence of calibration targets of known dimensions, the approach can be realized by using stereo correspondences alone.

Keywords

Underwater camera calibration Underwater imaging 3D reconstruction 

Notes

Acknowledgments

This research has been supported by the German Federal State of Mecklenburg-Western Pomerania and the European Social Fund under grant ESF/IV-BM-B35-0006/12.

References

  1. 1.
    Agrawal, A., Ramalingam, S., Taguchi, Y., Chari, V.: A theory of multi-layer flat refractive geometry. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3346–3353 (2012)Google Scholar
  2. 2.
    Bräuer-Burchardt, C., Kühmstedt, P., Notni, G.: Combination of air- and water-calibration for a fringe projection based underwater 3D-scanner. In: Effenberg, A.O., Azzopardi, G., Petkov, N. (eds.) CAIP 2015. LNCS, vol. 9257, pp. 49–60. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-23117-4_5 CrossRefGoogle Scholar
  3. 3.
    Chen, X., Yang, Y.-H.: Two-view camera housing parameters calibration for multi-layer flat refractive interface. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 524–531 (2014)Google Scholar
  4. 4.
    Gedge, J., Gong, M., Yang, Y.-H.: Refractive epipolar geometry for underwater stereo matching. In: 2011 Canadian Conference on Computer and Robot Vision (CRV), pp. 146–152. IEEE (2011)Google Scholar
  5. 5.
    Kang, L., Wu, L., Wei, Y., Yang, Z.: Theory of multi-level refractive geometry. Electron. Lett. 51(9), 688–690 (2015)CrossRefGoogle Scholar
  6. 6.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  7. 7.
    Sedlazeck, A., Koch, R.: Calibration of housing parameters for underwater stereo-camera rigs. In: Proceedings of the British Machine Vision Conference, pp. 118.1–118.11. BMVA Press (2011)Google Scholar
  8. 8.
    Shortis, M.: Calibration techniques for accurate measurements by underwater camera systems. Sensors 15(12), 30810–30826 (2015)CrossRefGoogle Scholar
  9. 9.
    Treibitz, T., Schechner, Y.Y., Kunz, C., Singh, H.: Flat refractive geometry. IEEE Trans. Pattern Anal. Mach. Intell. 34(1), 51–65 (2012)CrossRefGoogle Scholar
  10. 10.
    Yamashita, A., Higuchi, H., Kaneko, T., Kawata, Y.: Three dimensional measurement of object’s surface in water using the light stripe projection method. In: Proceedings of 2004 IEEE International Conference on Robotics and Automation, ICRA 2004, vol. 3, pp. 2736–2741. IEEE (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Fraunhofer Institute for Computer Graphics Research IGDRostockGermany
  2. 2.Institute for Computer ScienceUniversity of RostockRostockGermany

Personalised recommendations