Dense reconstruction by zooming

  • C. Delherm
  • J. M. Lavest
  • M. Dhome
  • J. T. Lapresté
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1065)


Reconstruction by zooming is not an unachievable task. As it has been previously demonstrated, axial stereovision technics allows to infer 3D information, but involves very small triangulation angles. Accurate calibration, data matching and reconstruction have to be performed to obtain satisfactory modelling results. In this paper, a new approach is proposed to realize dense reconstruction using a static camera equipped with a zoom lens.

The proposed algorithm described in the following sections is divided in three major steps:

  • First of all, the matching problem is solved using a correlation algorithm that explicitely takes into account the zooming effect through the images set. An intensity-based multiscale algorithm is applied to the feature points in the first image, to obtain unique point correspondences in all the other images.

  • Then, using pixels matched by the previous method, an iterative process is proposed to obtain a sub-pixel matching.

  • Finally, the 3D surface is reconstructed using image point correspondances. The modelling algorithm does not require any explicit calibration model and the computations involved are straightforward. This approach uses several images of accurate regular grids placed on a micrometric table, as a calibration process [1]. Complete experiments on real data are provided and show that it is possible to compute 3D dense information from a zooming image set.


Correlation Dense Reconstruction Axial Stereovision Implicit Calibration 


  1. 1.
    JM Lavest, C Delherm, B Peuchot, and N Daucher. Implicit Reconstruction by Zooming. To appear in Computer Vision, Graphics and Image Processing, 1995.Google Scholar
  2. 2.
    JM Lavest, G Rives, and M Dhome. 3D Reconstruction by Zooming. IEEE Transactions on Robotics and Automation, 9(2):196–208, April 1993.Google Scholar
  3. 3.
    JM Lavest, G Rives, and M Dhome. Modeling an Object of Revolution by Zooming. IEEE Trans. on Robotics and Automation, 11(2):267–271, April 1995.Google Scholar
  4. 4.
    HA Martins, JR Birk, and RB Kelley. Camera Models Based on Data from Two Calibration Planes. Computer Graphics and Image Processing, 17:173–180, 1981.Google Scholar
  5. 5.
    KD Gremban, CH Thorpe, and T Kanade. Geometric Camera Calibration using Systems of Linear Equations. Proc. of IEEE Robotics and Automation, pages 562–567, 1988.Google Scholar
  6. 6.
    GQ Wei and SD Ma. Two Plane Camera Calibration: a Unified Model, in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pages 133–138, June 1991.Google Scholar
  7. 7.
    B Peuchot. Utilisation de détecteurs sub-pixels dans la modélisation d'une caméra. 9ème congrès AFCET RFIA, pages 691–695, Paris, January 1994.Google Scholar
  8. 8.
    P Brand, R Mohr, and P Bobet. Distorsions optiques: correction dans un modèle projectif. 9ème congrès AFCET RFIA, pages 87–98, Paris, January 1994.Google Scholar
  9. 9.
    R Deriche and G Giraudon. A Computational Approach for Corner and Vertex Detection. International Journal of Computer Vision, 10(2):101–124, 1993.Google Scholar
  10. 10.
    HA Beyer. Accurate Calibration of CCD Cameras, in Proc. of Conference on Computer Vision and Pattern Recognition, Urbana Champaign, USA, pages 96–101, 1992.Google Scholar
  11. 11.
    B Peuchot. Camera Virtual Equivalent Model, 0.01 Pixel Detector. Computerized Medical Imaging and Graphics, 17(4–5):289–294, 1993.Google Scholar
  12. 12.
    CS Zhao. Reconstruction de surfaces tridimensionnelles en vision par ordinateur. Thèse de doctorat, Institut National Polytechnique de Grenoble, France, 1993.Google Scholar
  13. 13.
    C Delherm, JM Lavest, B Peuchot, and N Daucher. Reconstruction implicite par zoom. To appear in Traitement du signal, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • C. Delherm
    • 1
  • J. M. Lavest
    • 1
  • M. Dhome
    • 1
  • J. T. Lapresté
    • 1
  1. 1.Laboratoire des Sciences et Matériaux pour l'Electronique, et d'Automatique URA 1793 of the CNRSUniversité Blaise-Pascal de Clermont-FerrandAubière CedexFrance

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