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Abstract

A new algorithm for model based registration is presented that optimizes both position and surface normal information of the shapes being registered. This algorithm extends the popular Iterative Closest Point (ICP) algorithm by incorporating the surface orientation at each point into both the correspondence and registration phases of the algorithm. For the correspondence phase an efficient search strategy is derived which computes the most probable correspondences considering both position and orientation differences in the match. For the registration phase an efficient, closed-form solution provides the maximum likelihood rigid body alignment between the oriented point matches. Experiments by simulation using human femur data demonstrate that the proposed Iterative Most Likely Oriented Point (IMLOP) algorithm has a strong accuracy advantage over ICP and has increased ability to robustly identify a successful registration result.

Keywords

point cloud registration Fisher distribution PD tree 

References

  1. 1.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the icp algorithm. In: Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling, pp. 145–152 (2001)Google Scholar
  2. 2.
    Besl, P., McKay, N.D.: A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 239–256 (1992)CrossRefGoogle Scholar
  3. 3.
    Arun, K., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-d point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence 9, 698–700 (1987)CrossRefGoogle Scholar
  4. 4.
    Pulli, K.: Multiview registration for large data sets. In: Proceedings of the Second International Conference on 3-D Digital Imaging and Modeling, pp. 160–168 (1999)Google Scholar
  5. 5.
    Lara, C., Romero, L., Calderón, F.: A robust iterative closest point algorithm with augmented features. In: Gelbukh, A., Morales, E.F. (eds.) MICAI 2008. LNCS (LNAI), vol. 5317, pp. 605–614. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Kang, X., Armand, M., Otake, Y., Yau, W.P., Cheung, P., Hu, Y., Taylor, R.: Robustness and accuracy of feature-based single image 2D-3D registration without correspondences for image-guided intervention. IEEE Transactions on Biomedical Engineering 61, 149–161 (2014)CrossRefGoogle Scholar
  7. 7.
    Liu, X., Cevikalp, H., Fitzpatrick, J.M.: Marker orientation in fiducial registration. In: Medical Imaging 2003, International Society for Optics and Photonics, pp. 1176–1185 (2003)Google Scholar
  8. 8.
    Banerjee, A., Dhillon, I.S., Ghosh, J., Sra, S., Ridgeway, G.: Clustering on the unit hypersphere using von mises-fisher distributions. Journal of Machine Learning Research 6 (2005)Google Scholar
  9. 9.
    Mardia, K., Jupp, P.: Directional Statistics. Wiley Series in Probability and Statistics. Wiley (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Seth Billings
    • 1
  • Russell Taylor
    • 1
  1. 1.Department of Computer ScienceJohns Hopkins UniversityBaltimoreUSA

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