Precise Registration of 3D Images Acquired from a Hand-Held Visual Sensor

  • Benjamin Coudrin
  • Michel Devy
  • Jean-José Orteu
  • Ludovic Brèthes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6915)


This paper presents a method for precise registration of 3D images acquired from a new sensor for 3D digitization moved manually by an operator around an object. The system is equipped with visual and inertial devices and with a speckle pattern projector. The presented method has been developed to address the problem that a moving speckle pattern during a sequence prevents from correlating points between images acquired from two successive viewpoints. So several solutions are proposed, based on images acquired with a moving speckle pattern. It improves ICP-based methods classically used for precise registration of two clouds of 3D points.


Iterative Close Point Rigid Transformation Iterative Close Point Algorithm Precise Registration Iterative Close Point Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-d point sets. IEEE Trans. on PAMI 9 (1997)Google Scholar
  2. 2.
    Besl, P., McKay, N.: A method for regtration of 3-d shapes. IEEE Trans. on PAMI 14 (1992)Google Scholar
  3. 3.
    Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vision Comput. 10(3), 145–155 (1992)CrossRefGoogle Scholar
  4. 4.
    Coudrin, B., Devy, M., Orteu, J.J., Brèthes, L.: An innovative hand-held visual digitizing system for 3d modelling. Optics and Lasers in Engineering (2011)Google Scholar
  5. 5.
    Devernay, F., Faugeras, O.: Shape from stereo using fine correlation: Method and error analysis. Technical report, INRIA Sophia-Antipolis (2000)Google Scholar
  6. 6.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000) ISBN: 0521623049zbMATHGoogle Scholar
  7. 7.
    Johnson, A., Herbert, M.: Surface registration by matching oriented points. In: Proc. Int. Conf. on 3-D Digital Imaging and Modeling (3DIM) (1997)Google Scholar
  8. 8.
    Li, H., Hartley, R.I.: Five-point motion estimation made easy. In: Proc. Int. Conf. on Pattern Rerognition, ICPR (2006)Google Scholar
  9. 9.
    Low, K.: Linear least-squares optimization for point-to-plane icp surface registration. Technical report, University of North Carolina at Chapel Hill (2004)Google Scholar
  10. 10.
    Neugebauer, P.: Geometrical cloning of 3d objects via simultaneous registration of multiple range images. In: Proc. Int. Conf. on Shape Modeling and Applications (1997)Google Scholar
  11. 11.
    Park, S.Y., Subbarao, M.: A fast point-to-tangent plane technique for multi-view registration. In: Proc 4th Int. Conf on 3-D Digital Imaging and Modeling, pp. 276–284 (2003)Google Scholar
  12. 12.
    Sandhu, S.D.R., Tannenbaum, A.: Particle filtering for registration of 2d and 3d point sets with stochastic dynamics. In: Proc. Conf. Computer Vision and Pattern Recognition (2008)Google Scholar
  13. 13.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the icp algorithm. In: Proc. Int. Conf on 3-D Digital Imaging and Modeling (3DIM) (2001)Google Scholar
  14. 14.
    Segal, A., Haehnel, D., Thrun, S.: Generalized-icp. In: Proc. Conf. Robotics: Science and Systems (RSS), Seattle, USA (June 2009)Google Scholar
  15. 15.
    Studholme, C., Hill, D., Hawkes, D.: An overlap invariant entropy measure of 3d medical image alignment. Pattern Recognition 32 (1999)Google Scholar
  16. 16.
    Sutton, M.A., Orteu, J.J., Schreier, H.: Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts,Theory and Applications. Springer Publishing Company, Incorporated (2009) Google Scholar
  17. 17.
    Suveg, I., Vosselman, G.: Mutual information based evaluation of 3d building models. In: Proc. Int. Conf. on Pattern Rerognition (ICPR), vol. 3 (2002)Google Scholar
  18. 18.
    Thévenaz, P., Blu, T., Unser, M.: Interpolation revisited. IEEE Trans. on Medical Imaging 19(7), 739–758 (2000)CrossRefGoogle Scholar
  19. 19.
    Triggs, B., McLauchlan, P., Hartley, R., Fitzgibbon, A.: Bundle adjustment a modern synthesis. In: Proc. Int. Workshop on Vision Algorithms, with ICCV 1999 (1999)Google Scholar
  20. 20.
    Viola, P., Wells, W.M.: Alignment by maximization of mutual information. Int. Journal on Computer Vision, IJCV (1997)Google Scholar
  21. 21.
    Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. Journal on Computer Vision (IJCV) 13 (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Benjamin Coudrin
    • 1
    • 2
    • 3
    • 4
    • 5
  • Michel Devy
    • 2
    • 3
  • Jean-José Orteu
    • 4
    • 5
  • Ludovic Brèthes
    • 1
  1. 1.NOOMEOLabège CEDEXFrance
  2. 2.CNRS; LAASToulouseFrance
  3. 3.UPS, INSA, INP, ISAE; LAAS-CNRSUniversité de ToulouseToulouseFrance
  4. 4.Mines Albi; ICAUniversité de ToulouseAlbiFrance
  5. 5.Ecoles des mines AlbiAlbiFrance

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