Shape Rectification of 3D Data Obtained by a Moving Range Sensor by using Image Sequences

  • Atsuhiko Banno
  • Katsushi Ikeuchi

For a large object, scanning from the air is one of the most efficient methods of obtaining 3D data. We have been developing a novel 3D measurement system, the Flying Laser Range Sensor (FLRS), in which a range sensor is suspended beneath a balloon. The obtained data, however, have some distortion due to movement during the scanning process. Then we propose a novel method to rectify the shape data obtained by a moving range sensor. The method rectifies them by using image sequences. We are conducting the Digital Bayon Project, in which our algorithm is actually applied for range data processing and the results show the effectiveness of our methods. Our proposed method is applicable not only to our FLRS, but also to a general moving range sensor.


Interest Point Factorization Method Camera Motion Range Sensor Scanner Unit 
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  1. [1]
  2. [2]
  3. [3]
    P. J. Besl and N. D. McKay. A method for registration of 3-D shapes. IEEE Trans. on PAMI, 14:239-256, 1992.Google Scholar
  4. [4]
    G. Blais and M. D. Levine. Registering multiview range data to crate 3D computer objects. IEEE Trans. on PAMI, 17(8):820-824, 1995.Google Scholar
  5. [5]
    D. Brown. The bundle adjustment - progress and prospect. In XIII Congress of the ISPRS, Helsinki, 1976.Google Scholar
  6. [6]
    Y. Chen and G. Medioni. Object modeling by registration of multiple range images. Image and Vision Computing, 10(3):145-155, 1992.Google Scholar
  7. [7]
    S. Christy and R. Horaud. Euclidean shape and motion from multiple perspective views by affine iterations. IEEE Trans. on PAMI, 18(11):1098-1104,1996.Google Scholar
  8. [8]
    J. Costeira and T. Kanade. A multi-body factorization method for motion analysis. In Proc. of ICCV1995, pages 1071-1076, 1995.Google Scholar
  9. [9]
    J. H. Friedman, J. L. Bentley, and R. A. Finkel. An algorithm for finding best-matches in logarithmic time. ACM Trans. on Mathematical Software, 3(3):209-226, 1977.MATHCrossRefGoogle Scholar
  10. [10]
    P. Gill, W. Murray, and M. Wright. Practical Optimization. Academic Press, London, 1981.MATHGoogle Scholar
  11. [11]
    A. Gruber and Y. Weiss. Multibody factorization with uncertainty and missing data using the em algorithm. In Proc. of CVPR2004, volume 1, pages 707-714, 2004.Google Scholar
  12. [12]
    M. Han and T. Kanade. Perspective factorization methods for euclidean reconstruction. Technical Report :CMU-RI-TR-99-22, Robotics Institute, Carnegie Mellon University, 1999.Google Scholar
  13. [13]
    C. Harris and M. Stephens. A combined corner and edge detector. In Proc. of Alvey Vision Conference, pages 147-152, 1988.Google Scholar
  14. [14]
    Y. Hirota, T. Masuda, R. Kurazume, K. Ogawara, K. Hasegawa, and K. Ikeuchi. Designing a laser range finder which is suspended beneath a balloon. In Proc. of ACCV2004, volume 2, pages 658-663, 2004.Google Scholar
  15. [15]
    K. Ikeuchi, K. Hasegawa, A. Nakazawa, J. Takamatsu, T. Oishi, and T. Masuda. Bayon digital archival project. In Proc. of VSMM2004, pages 334-343, 2004.Google Scholar
  16. [16]
    K. Ikeuchi, A. Nakazawa, K. Hasegawa, and T. Ohishi. The great buddha project: Modeling cultural heritage for VR systems through observation. In Proc. of ISMAR2003, 2003.Google Scholar
  17. [17]
    D. A. Jacobs. The State of the Art in Numerical Analysis. Academic Press, London, 1977.Google Scholar
  18. [18]
    D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.CrossRefGoogle Scholar
  19. [19]
    D. W. Marquardt. An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11:431-441, 1963.MATHCrossRefMathSciNetGoogle Scholar
  20. [20]
    T. Masuda, Y. Hirota, K. Nishino, and K. Ikeuchi. Simultaneous determination of registration and deformation parameters among 3D range images. In Proc. of 3DIM2005, pages 369-376, 2005.Google Scholar
  21. [21]
    D. Miyazaki, T. Oishi, T. Nishikawa, R. Sagawa, K. Nishino, T. Tomomatsu, Y. Yakase, and K. Ikeuchi. The great buddha project: Modelling cultural heritage through observation. In Proc. of VSMM2000, pages 138-145, 2000.Google Scholar
  22. [22]
    H. P. Moravec. Towards automatic visual obstacle avoidance. In Proc. 5th International Joint Conference on Artificial Intelligence, page 584, 1977.Google Scholar
  23. [23]
    T. Morita and T. Kanade. A sequential factorization method for recovering shape and motion from image streams. IEEE Trans. on PAMI, 19(8):858-867, 1997.Google Scholar
  24. [24]
    P. Neugebauer. Geometrical cloning of 3D objects via simultaneous registration of multiple range images. In Proc. of the International Conference on Shape Modeling and Application, pages 130-139, 1997.Google Scholar
  25. [25]
    C. Poelmann and T. Kanade. A paraperspective factorization method for shape and motion recovery. IEEE Trans. on PAMI, 19(3):206-218, 1997.Google Scholar
  26. [26]
    E. Polak. Computational Methods in Optimization. Academic Press, New York, 1971.Google Scholar
  27. [27]
    W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C. Cambridge University Press, 1988.Google Scholar
  28. [28]
    S. Rusinkiewicz and M. Levoy. Efficient variant of the ICP algorithm. In Proc. of 3DIM2001, pages 145-152, 2001.Google Scholar
  29. [29]
    S. M. Smith and M. Brady. SUSAN - a new approach to low level image processing. IJCV, 23(1):45-78, 1997.CrossRefGoogle Scholar
  30. [30]
    J. Stoer and R.Bulirsh. Introduction to Numerical Analysis. Springer-Verlag, New York, 1980.Google Scholar
  31. [31]
    S. Thrun, M. Diel, and D. Haehnel. Scan alignment and 3-D surface modeling with a helicopter platform. In Proc. of the 4th International Conference on Field and Service Robotics, 2003.Google Scholar
  32. [32]
    C. Tomasi and T. Kanade. Shape and motion from image streams under orthography: a factorization method. IJCV, 9(2):137-154, 1992.CrossRefGoogle Scholar
  33. [33]
    J. Visnovcova, L. Zhang, and A. Gruen. Generating a 3D model of a bayon tower using non-metric imagery. In Proc. of the International Workshop Recreating the Past -Visualization and Animation of Cultural Heritage, 2001.Google Scholar
  34. [34]
    E. Walter and L. Prontazo. Identification of Parametric Models from Experimental Data. Springer, 1997.Google Scholar
  35. [35]
    Z. Zhang. Iterative point matching for registration of free-form curves and surfaces. IJCV, 13:119-152, 1994.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Atsuhiko Banno
  • Katsushi Ikeuchi
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoMeguro-kuJapan

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