Accounting for Anisotropic Noise in Fine Registration of Time-of-Flight Range Data with High-Resolution Surface Data

  • L. Maier-Hein
  • M. Schmidt
  • A. M. Franz
  • T. R. dos Santos
  • A. Seitel
  • B. Jähne
  • J. M. Fitzpatrick
  • H. P. Meinzer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6361)

Abstract

Time-of-Flight (ToF) sensors have become a considerable alternative to conventional surface acquisition techniques such as laser range scanning and stereo vision. Application of ToF cameras for the purpose of intra-operative registration requires matching of the noisy surfaces generated from ToF range data onto pre-interventionally acquired high-resolution surfaces. The contribution of this paper is two-fold: Firstly, we present a novel method for fine rigid registration of noisy ToF data with high-resolution surface meshes taking into account both, the noise characteristics of ToF cameras and the resolution of the target mesh. Secondly, we introduce an evaluation framework for assessing the performance of ToF registration methods based on physically realistic ToF range data generated from a virtual scence. According to experiments within the presented evaluation framework, the proposed method outperforms the standard ICP algorithm with respect to correspondence search and transformation computation, leading to a decrease in the target registration error (TRE) of more than 70%.

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References

  1. 1.
    Balachandran, R., Fitzpatrick, J.M.: Iterative solution for rigid-body point-based registration with anisotropic weighting. In: SPIE Medical Imaging, vol. 7261, p. 72613D (2009)Google Scholar
  2. 2.
    Kolb, A., et al.: Time-of-Flight sensors in computer graphics. In: Eurographics State of the Art Reports, pp. 119–134 (2009)Google Scholar
  3. 3.
    Besl, P.J., et al.: A method for registration of 3-d shapes. IEEE T. Pattern Anal. 14, 239–256 (1992)CrossRefGoogle Scholar
  4. 4.
    Maier-Hein, L., et al.: Iterative closest point algorithm in the presence of anisotropic noise. In: Bildverarbeitung für die Medizin (BVM), pp. 231–235. Springer, Heidelberg (2010)Google Scholar
  5. 5.
    Danilchenko, A., Fitzpatrick, J.M.: General approach to error prediction in point registration. In: SPIE Medical Imaging, vol. 7625, p. 76250 F (2010)Google Scholar
  6. 6.
    Schmidt, M., Jähne, B.: A physical model of Time-of-Flight 3D imaging systems, including suppression of ambient light. In: Kolb, A., Koch, R. (eds.) Dyn3D 2009. LNCS, vol. 5742, pp. 1–15. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Erz, M., Jähne, B.: Radiometric and spectrometric calibrations, and distance noise measurement of ToF cameras. In: Kolb, A., Koch, R. (eds.) Dyn3D 2009. LNCS, vol. 5742, pp. 28–41. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • L. Maier-Hein
    • 1
  • M. Schmidt
    • 2
  • A. M. Franz
    • 1
  • T. R. dos Santos
    • 1
  • A. Seitel
    • 1
  • B. Jähne
    • 2
  • J. M. Fitzpatrick
    • 3
  • H. P. Meinzer
    • 1
  1. 1.Div. Medical and Biological InformaticsGerman Cancer Research Center 
  2. 2.Heidelberg Collaboratory for Image ProcessingUniversity of HeidelbergGermany
  3. 3.Dept. Electrical Engineering and Computer ScienceVanderbilt UniversityUSA

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