Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds

  • Minh-Tri Pham
  • Oliver J. Woodford
  • Frank Perbet
  • Atsuto Maki
  • Riccardo Gherardi
  • Björn Stenger
  • Roberto Cipolla
Part of the Studies in Computational Intelligence book series (SCI, volume 411)


This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this space—the SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach.


Point Cloud Visual Word Registration Rate Inference Method Training Feature 
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.


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  1. 1.
    Toshiba CAD model point clouds datasetGoogle Scholar
  2. 2.
    Agrawal, M.: A Lie algebraic approach for consistent pose registration for general euclidean motion. In: Proc. Int. Conf. on Intelligent Robot and Systems, pp. 1891–1897 (2006)Google Scholar
  3. 3.
    Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A Log-Euclidean Polyaffine Framework for Locally Rigid or Affine Registration. In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 120–127. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)zbMATHCrossRefGoogle Scholar
  5. 5.
    Besl, P., McKay, N.: A method for registration of 3D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14(2) (1992)Google Scholar
  6. 6.
    Campbell, R.J., Flynn, P.J.: A survey of free-form object representation and recognition techniques. Computer Vision and Image Understanding 81, 166–210 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Cetingul, H.E., Vidal, R.: Intrinsic mean shift for clustering on Stiefel and Grassmann manifolds. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1896–1902 (2009)Google Scholar
  8. 8.
    Chen, H., Bhanu, B.: 3d free-form object recognition in range images using local surface patches. J. Pattern Recognition Letters 28, 1252–1262 (2007)CrossRefGoogle Scholar
  9. 9.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence 17, 790–799 (1995)CrossRefGoogle Scholar
  10. 10.
    Davies, P.I., Higham, N.J.: A Schur-Parlett algorithm for computing matrix functions. SIAM J. Matrix Anal. Appl. 25, 464–485 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: Efficient and robust 3D object recognition. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 998–1005 (2010)Google Scholar
  12. 12.
    Eggert, D.W., Lorusso, A., Fisher, R.B.: Estimating 3-d rigid body transformations: a comparison of four major algorithms. Machine Vision Application 9, 272–290 (1997)CrossRefGoogle Scholar
  13. 13.
    Ashbrook, A.P., Fisher, R.B., Robertson, C., Werghi, N.: Finding Surface Correspondence for Object Recognition and Registration Using Pairwise Geometric Histograms. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, p. 674. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  14. 14.
    Fréchet, M.: Les éléments aléatoires de nature quelconque dans un espace distancié. Ann. Inst. H. Poincaré 10, 215–310 (1948)Google Scholar
  15. 15.
    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
  16. 16.
    Gall, J., Lempitsky, V.: Class-specific hough forests for object detection. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1022–1029 (June 2009)Google Scholar
  17. 17.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(5), 433–449 (1999)CrossRefGoogle Scholar
  18. 18.
    Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3d shape descriptors. In: Proc. Eurographics/ACM SIGGRAPH Symp. on Geometry Processing, pp. 156–164 (2003)Google Scholar
  19. 19.
    Khoshelham, K.: Extending generalized Hough transform to detect 3D objects in laser range data. In: Workshop on Laser Scanning, vol. XXXVI, pp. 206–210 (2007)Google Scholar
  20. 20.
    Knopp, J., Prasad, M., Willems, G., Timofte, R., Van Gool, L.: Hough Transform and 3D SURF for Robust Three Dimensional Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 589–602. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. Int. J. Computer Vision 77(1-3), 259–289 (2008)CrossRefGoogle Scholar
  22. 22.
    Mamic, G., Bennamoun, M.: Representation and recognition of 3d free-form objects. Digital Signal Processing 12(1), 47–76 (2002)CrossRefGoogle Scholar
  23. 23.
    Mian, A.S., Bennamoun, M., Owens, R.A.: Automatic correspondence for 3D modeling: an extensive review. Int. J. Shape Modeling 11(2), 253–291 (2005)zbMATHCrossRefGoogle Scholar
  24. 24.
    Mian, A.S., Bennamoun, M., Owens, R.: Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(10), 1584–1601 (2006)CrossRefGoogle Scholar
  25. 25.
    Moakher, M.: Means and averaging in the group of rotations. SIAM J. Matrix Anal. Appl. 24, 1–16 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Mundy, J.L.: Object Recognition in the Geometric Era: A Retrospective. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 3–28. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  27. 27.
    Okada, R.: Discriminative generalized hough transform for object dectection. In: Proc. Int. Conf. on Computer Vision, pp. 2000–2005 (October 2009)Google Scholar
  28. 28.
    Opelt, A., Pinz, A., Zisserman, A.: Learning an alphabet of shape and appearance for multi-class object detection. Int. J. Computer Vision 80(1) (2008)Google Scholar
  29. 29.
    Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape distributions. ACM Trans. Graph. 21, 807–832 (2002)CrossRefGoogle Scholar
  30. 30.
    Pelletier, B.: Kernel density estimation on Riemannian manifolds. Statistics Probability Letters 73(3), 297–304 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Pennec, X.: Intrinsic statistics on Riemannian manifolds: Basic tools for geometric measurements. JMIV 25(1), 127–154 (2006)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Pennec, X., Ayache, N.: Uniform distribution, distance and expectation problems for geometric features processing. J. Math. Imaging Vis. 9, 49–67 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  33. 33.
    Petrelli, A., Di Stefano, L.: On the repreatability of the local reference frame for partial shape matching. In: Proc. Int. Conf. on Computer Vision (2011)Google Scholar
  34. 34.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (fpfh) for 3d registration. In: Proc. Int. Conf. Robotics and Automation, pp. 3212–3217 (2009)Google Scholar
  35. 35.
    Saupe, D., Vranic, D.V.: 3D Model Retrieval with Spherical Harmonics and Moments. In: Radig, B., Florczyk, S. (eds.) DAGM 2001. LNCS, vol. 2191, p. 392. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  36. 36.
    Schramm, É., Schreck, P.: Solving geometric constraints invariant modulo the similarity group. In: Int. Conf. on Computational Science and Applications, pp. 356–365 (2003)Google Scholar
  37. 37.
    Shotton, J.D.J., Blake, A., Cipolla, R.: Multiscale categorical object recognition using contour fragments. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(7), 1270–1281 (2008)CrossRefGoogle Scholar
  38. 38.
    Srivastava, A., Klassen, E.: Monte Carlo extrinsic estimators of manifold-valued parameters. IEEE Trans. on Signal Processing 50(2), 299–308 (2002)CrossRefGoogle Scholar
  39. 39.
    Subbarao, R., Meer, P.: Nonlinear mean shift for clustering over analytic manifolds. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. I, pp. 1168–1175 (2006)Google Scholar
  40. 40.
    Subbarao, R., Meer, P.: Nonlinear mean shift over Riemannian manifolds. Int. J. Computer Vision 84(1) (2009)Google Scholar
  41. 41.
    Tombari, F., Di Stefano, L.: Object recognition in 3D scenes with occlusions and clutter by Hough voting. In: Proc. Pacifc-Rim Symp. on Image and Video Technology, pp. 349–355 (2010)Google Scholar
  42. 42.
    Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Proc. European Conf. on Computer Vision (2010)Google Scholar
  43. 43.
    Vogiatzis, G., Hernández, C.: Video-based, real-time multi view stereo. Image and Vision Computing 29(7), 434–441 (2011)CrossRefGoogle Scholar
  44. 44.
    Woodford, O.J., Pham, M.-T., Maki, A., Perbet, F., Stenger, B.: Demisting the Hough transform for 3D shape recognition and registration. In: British Machine Vision Conference (2011)Google Scholar
  45. 45.
    Roger, P.: Woods. Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation. NeuroImage, 18(3):769–788 (2003)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  • Minh-Tri Pham
    • 1
  • Oliver J. Woodford
    • 1
  • Frank Perbet
    • 1
  • Atsuto Maki
    • 1
  • Riccardo Gherardi
    • 1
  • Björn Stenger
    • 1
  • Roberto Cipolla
    • 2
  1. 1.Toshiba Research Europe LtdCambridgeUK
  2. 2.Department of EngineeringUniversity of CambridgeCambridgeUK

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