A Fast Simultaneous Alignment of Multiple Range Images

  • Takeshi Oishi
  • Atsushi Nakazawa
  • Ryo Kurazume
  • Katsushi Ikeuchi

This chapter describes a fast, simultaneous alignment method for a large number of range images. Generally the most time-consuming task in aligning range images is searching corresponding points. The fastest searching method is the “Inverse Calibration” method. However, this method requires pre-computed lookup tables and precise sensor parameters. We propose a fast searching method using “index images,” which work as look-up tables and are rapidly created without any sensor parameters by using graphics hardware. To accelerate the computation to estimate rigid transformations, we employed a linear error evaluation method. When the number of range images increases, the computation time for solving the linear equations becomes too long because of the large size of the coefficient matrix. On the other hand, the coefficient matrix has the characteristic of becoming sparser as the number of range images increases. Thus, we applied the Incomplete Cholesky Conjugate Gradient (ICCG) method to solve the equations and found that the ICCG greatly accelerates the matrix operation by pre-conditioning the coefficient matrix. Some experimental results in which a large number of range images are aligned demonstrate the effectiveness of our method.


Lookup Table Mesh Model Range Image Iterative Close Point Cholesky Decomposition 


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  1. [1]
    P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 14(2), 239-256,1992.CrossRefGoogle Scholar
  2. [2]
    Y. Chen and G. Medioni, “Object modeling by registration of multiple range images,” Image and Vision Computing, 10(3), pp. 145-155, 1992.CrossRefGoogle Scholar
  3. [3]
    G. Blais and M. Levine, “Registering Multiview Range Data to Create 3D Computer Objects,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 17, No. 8, 1995.Google Scholar
  4. [4]
    S. Rusinkiewicz, O. Hall-Holt and M. Levoy, “Real-Time 3D Model Acquisition,” ACM Transactions on Graphics. 21(3): 438-446, July 2002.CrossRefGoogle Scholar
  5. [5]
    T. Masuda, K. Sakaue and N. Yokoya, “Registration and Integration of Multiple Range Images for 3-D Model Construction,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1996.Google Scholar
  6. [6]
    P. J. Neugebauer. “Reconstruction of Real-World Objects via Simultaneous Registration and Robust Combination of Multiple Range Images.” Int’l J. Shape Modeling, 3(1&2):71-90, 1997.CrossRefGoogle Scholar
  7. [7]
    R. Bergevin, M. Soucy, H. Gagnon, and D. Laurendeau. To-wards a general multi-view registration technique. IEEE Trans. on Pattern Analysis and Machine Intelligence, 18(5):540-547, May 1996.CrossRefGoogle Scholar
  8. [8]
    R. Benjemaa and F. Schmitt. “Fast global registration of 3d sampled surfaces using a multi-z-buffer technique,” Proc. Int’l Conf. Recent Advances in 3-D Digital Imaging and Modeling, May 1997, pp. 113-120.Google Scholar
  9. [9]
    Z. Zhang, “Iterative point matching for registration of free-form curves and surfaces,” Int’l J. Computer Vision, 13(2):119-152, 1994.CrossRefGoogle Scholar
  10. [10]
    K. Nishino and K. Ikeuchi, “Robust Simultaneous Registration of Multiple Range Images,” Proc. Fifth Asian Conf. Computer Vision, Jan. 2002, pp. 454-461.Google Scholar
  11. [11]
    D. A. Simon, M. Hebert and T. Kanade, “Realtime 3-D pose estimation using a high-speed range sensor,” Proc. IEEE Int’l Conf. Robotics and Automation, May 1994, pp. 2235-2241.Google Scholar
  12. [12]
    M. Greenspan and G. Godin, “A Nearest Neighbor Method for Efficient ICP,” Proc. Int’l Conf. 3D Digital Imaging and Modeling (3DIM), 2001, pp. 161-168.Google Scholar
  13. [13]
    R. Sagawa, T. Masuda and K. Ikeuchi, “Effective Nearest Neighbor Search for Aligning and Merging Range Images,” Proc. Int’l Conf. 3-D Digital Imaging and Modeling (3DIM), 2003, pp. 79-86.Google Scholar
  14. [14]
    S. Rusinkiewicz and M. Levoy, “Efficient variants of the IPC algorithm,” Proc. Int’l Conf. 3-D Digital Imaging and Modeling (3DIM), May 2001, pp. 145-152.Google Scholar
  15. [15]
    Y. Saad, Iterative methods for sparse linear system Series, Computer Science, PWS, 1996.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Takeshi Oishi
  • Atsushi Nakazawa
  • Ryo Kurazume
  • Katsushi Ikeuchi
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoMeguro-kuJapan

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