Multi-view Point Cloud Registration Using Affine Shape Distributions

  • Jia DuEmail author
  • Wei Xiong
  • Wenyu Chen
  • Jierong Cheng
  • Yue Wang
  • Ying Gu
  • Shue-Ching Chia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)


Registration is crucial for the reconstruction of multi-view single plane illumination microscopy. By using fluorescent beads as fiduciary markers, this registration problem can be reduced to the problem of point clouds registration. We present a novel method for registering point clouds across views. This is based on a new local geometric descriptor - affine shape distribution - to represent the random spatial pattern of each point and its neighbourhood. To enhance its robustness and discriminative power against the missing data and outliers, a permutation and voting scheme based on affine shape distributions is developed to establish putative correspondence pairs across views. The underlying affine transformations are estimated based on the putative correspondence pairs via the random sample consensus. The proposed method is evaluated on three types of datasets including 3D random points, benchmark datasets and datasets from multi-view microscopy. Experiments show that the proposed method outperforms the state-of-the-arts when both point sets are contaminated by extremely large amount of outliers. Its robustness against the anisotropic z-stretching is also demonstrated in the registration of multi-view microscopy data.


Point Cloud Affine Transformation Point Pattern Iterative Close Point Iterative Close Point 
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.


  1. 1.
    Swoger, J., Verveer, P., Greger, K., Huisken, J., Stelzer, E.H.: Multi-view image fusion improves resolution in three-dimensional microscopy. Opt. Express 15, 8029–8042 (2007)CrossRefGoogle Scholar
  2. 2.
    Keller, P.J., Schmidt, A.D., Santella, A., Khairy, K., Bao, Z., Wittbrodt, J., Stelzer, E.H.: Fast, high-contrast imaging of animal development with scanned light sheet-based structured-illumination microscopy. Nat. Meth. 7, 637–642 (2010)CrossRefGoogle Scholar
  3. 3.
    Preibisch, S., Saalfeld, S., Schindelin, J., Tomancak, P.: Software for bead-based registration of selective plane illumination microscopy data. Nat. Meth. 7, 418–419 (2010)CrossRefGoogle Scholar
  4. 4.
    Krzic, U., Gunther, S., Saunders, T.E., Streichan, S.J., Hufnagel, L.: Multiview light-sheet microscope for rapid in toto imaging. Nat. Meth. 9, 730–733 (2012)CrossRefGoogle Scholar
  5. 5.
    Schmid, B., Shah, G., Scherf, N., Weber, M., Thierbach, K., Campos, C.P., Roeder, I., Aanstad, P., Huisken, J.: High-speed panoramic light-sheet microscopy reveals global endodermal cell dynamics. Nat. Commun. 4, 2207 (2013)CrossRefGoogle Scholar
  6. 6.
    Temerinac-Ott, M., Keuper, M., Burkhardt, H.: Evaluation of a new point clouds registration method based on group averaging features. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 2452–2455. IEEE (2010)Google Scholar
  7. 7.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Salvi, J., Matabosch, C., Fofi, D., Forest, J.: A review of recent range image registration methods with accuracy evaluation. Image Vis. Comput. 25, 578–596 (2007)CrossRefGoogle Scholar
  9. 9.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recogn. 37, 1–19 (2004)CrossRefzbMATHGoogle Scholar
  10. 10.
    Aldoma, A., Marton, Z.C., Tombari, F., Wohlkinger, W., Potthast, C., Zeisl, B., Rusu, R., Gedikli, S., Vincze, M.: Tutorial: point cloud library: three-dimensional object recognition and 6 DOF pose estimation. IEEE Robot. Autom. Mag. 19, 80–91 (2012)CrossRefGoogle Scholar
  11. 11.
    Besl, P., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)CrossRefGoogle Scholar
  12. 12.
    Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Comput. Vis. Image Underst. 89, 114–141 (2003)CrossRefzbMATHGoogle Scholar
  13. 13.
    Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32, 2262–2275 (2010)CrossRefGoogle Scholar
  14. 14.
    Jian, B., Vemuri, B.C.: Robust point set registration using gaussian mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1633–1645 (2011)CrossRefGoogle Scholar
  15. 15.
    Lian, W., Zhang, L.: Robust point matching revisited: a concave optimization approach. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 259–272. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  16. 16.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  17. 17.
    Aiger, D., Cohen-Or, N.J.: 4-points congruent sets for robust pairwise surface registration. ACM Trans. Graph. (TOG) 27, 85 (2008)CrossRefGoogle Scholar
  18. 18.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21, 433–449 (1999)CrossRefGoogle Scholar
  19. 19.
    Frome, A., Huber, D., Kolluri, R., Bülow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 224–237. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  20. 20.
    Leung, T.K., Burl, M.C., Perona, P.: Probabilistic affine invariants for recognition. In: Proceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 678–684. IEEE (1998)Google Scholar
  21. 21.
    Sur, F.: Robust matching in an uncertain world. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 2350–2353. IEEE (2010)Google Scholar
  22. 22.
    Dowson, D., Landau, B.: The frechet distance between multivariate normal distributions. J. Multiv. Anal. 12, 450–455 (1982)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Givens, C.R., Shortt, R.M., et al.: A class of wasserstein metrics for probability distributions. Mich. Math. J. 31, 231–240 (1984)CrossRefzbMATHMathSciNetGoogle Scholar
  24. 24.
    Grimson, W.E.L., Huttenlocher, D.P., Jacobs, D.W.: A study of affine matching with bounded sensor error. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 291–306. Springer, Heidelberg (1992) Google Scholar
  25. 25.
    Nakai, T., Kise, K., Iwamura, M.: Use of affine invariants in locally likely arrangement hashing for camera-based document image retrieval. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 541–552. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  26. 26.
    McIlroy, P., Izadi, S., Fitzgibbon, A.: 3D pose estimation using a projected dense dot pattern. IEEE Transactions on Visualization and Computer Graphics 20, 839–851 (2014)CrossRefGoogle Scholar
  27. 27.
    Preibisch, S.: The 7-angle spim dataset of drosophila. Accessed Mar 2014
  28. 28.
    Lindeberg, T.: Scale-space Theory in Computer Vision. Springer, Heidelberg (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jia Du
    • 1
    Email author
  • Wei Xiong
    • 1
  • Wenyu Chen
    • 1
  • Jierong Cheng
    • 1
  • Yue Wang
    • 1
  • Ying Gu
    • 1
  • Shue-Ching Chia
    • 1
  1. 1.Visual Computing DepartmentInstitute for Infocomm ResearchSingaporeSingapore

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