Unique Signatures of Histograms for Local Surface Description

  • Federico Tombari
  • Samuele Salti
  • Luigi Di Stefano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)


This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms. Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.


Feature Point Exponential Mapping Unique Signature Total Little Square Surface Match 
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.
    Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. PAMI 21, 433–449 (1999)Google Scholar
  2. 2.
    Mian, A., Bennamoun, M., Owens, R.: A novel representation and feature matching algorithm for automatic pairwise registration of range images. IJCV 66, 19–40 (2006)CrossRefGoogle Scholar
  3. 3.
    Conde, C., Rodríguez-Aragón, L.J., Cabello, E.: Automatic 3D face feature points extraction with spin images. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2006. LNCS, vol. 4142, pp. 317–328. Springer, Heidelberg (2006)Google Scholar
  4. 4.
    Wu, K., Levine, M.: Recovering parametrics geons from multiview range data. In: CVPR, pp. 159–166 (1994)Google Scholar
  5. 5.
    Solina, F., Bajcsy, R.: Recovery of parametric models from range images: the case for superquadrics with global deformations. PAMI 12, 131–147 (1990)Google Scholar
  6. 6.
    Stein, F., Medioni, G.: Structural indexing: Efficient 3D object recognition. PAMI 14, 125–145 (1992)Google Scholar
  7. 7.
    Chua, C.S., Jarvis, R.: Point signatures: A new representation for 3D object recognition. IJCV 25, 63–85 (1997)CrossRefGoogle Scholar
  8. 8.
    Sun, Y., Abidi, M.A.: Surface matching by 3D point’s fingerprint. ICCV 2, 263–269 (2001)Google Scholar
  9. 9.
    Novatnack, J., Nishino, K.: Scale-dependent/invariant local 3d shape descriptors for fully automatic registration of multiple sets of range images. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 440–453. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Chen, H., Bhanu, B.: 3D free-form object recognition in range images using local surface patches. Patt. Rec. Letters 28, 1252–1262 (2007)CrossRefGoogle Scholar
  11. 11.
    Koenderink, J., Doorn, A.: Surface shape and curvature scales. Image Vision Computing 8, 557–565 (1992)CrossRefGoogle Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Zhong, Y.: Intrinsic shape signatures: A shape descriptor for 3D object recognition. In: ICCV-WS: 3dRR (2009)Google Scholar
  14. 14.
    Bro, R., Acar, E., Kolda, T.: Resolving the sign ambiguity in the singular value decomposition. J. Chemometrics 22, 135–140 (2008)CrossRefGoogle Scholar
  15. 15.
    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. In: SIGGRAPH, pp. 71–78 (1992)Google Scholar
  16. 16.
    Mitra, N.J., Nguyen, A., Guibas, L.: Estimating surface normals in noisy point cloud data. Int. J. of Computational Geometry and Applications 14, 261–276 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  18. 18.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI 27, 1615–1630 (2005)Google Scholar
  19. 19.
    Ke, Y., Sukthankar, R.: Pca-sift: A more distinctive representation for local image descriptors. In: CVPR (2004)Google Scholar
  20. 20.
    Unnikrishnan, R., Hebert, M.: Multi-scale interest regions from unorganized point clouds. In: CVPR-WS: S3D (2008)Google Scholar
  21. 21.
    Davis, J., Nehab, D., Ramamoothi, R., Rusinkiewicz, S.: Spacetime stereo: A unifying framework for depth from triangulation. PAMI 27, 1615–1630 (2005)Google Scholar
  22. 22.
    Zhang, L., Curless, B., Seitz, S.: Spacetime stereo: Shape recovery for dynamic scenes. In: CVPR (2003)Google Scholar
  23. 23.
    Horn, B.K.P.: Closed-form solution of absolute orientation using unit quaternions. J. of the Optical Society of America A 4, 629–642 (1987)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Federico Tombari
    • 1
  • Samuele Salti
    • 1
  • Luigi Di Stefano
    • 1
  1. 1.CVLab - DEISUniversity of BolognaBolognaItaly

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