A Comparison Framework for 3D Object Classification Methods

  • S. Biasotti
  • D. Giorgi
  • S. Marini
  • M. Spagnuolo
  • B. Falcidieno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)


3D shape classification plays an important role in the process of organizing and retrieving models in large databases. Classifying shapes means to assign a query model to the most appropriate class of objects: knowledge about the membership of models to classes can be very useful to speed up and improve the shape retrieval process, by allowing the reduction of the candidate models to compare with the query.

The main contribution of this paper is the setting of a framework to compare the effectiveness of different query-to-class membership measures, defined independently of specific shape descriptors. The classification performances are evaluated against a set of popular 3D shape descriptors, using a dataset consisting of 14 classes made up of 20 objects each.


Shape Descriptor Query Model Query Object Shape Retrieval Comparison Framework 
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.
    Tangelder, J., Veltkamp, R.: A survey of content based 3d shape retrieval methods. In: Proc. Shape Modeling Applications 2004, pp. 145–156 (2004)Google Scholar
  2. 2.
    Bustos, B., Keim, D.A., Saupe, D., Schreck, T., Vranić, D.V.: Feature-based similarity search in 3D object databases. ACM Computing Surveys 37(4), 345–387 (2005)CrossRefGoogle Scholar
  3. 3.
    Sengupta, K., Boyer, K.L.: Organizing large structural mordelbases. IEEE Trans. on Pattern Analysis and Machine Intelligence 17(4), 321–332 (1995)CrossRefGoogle Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons Inc., Chichester (2001)MATHGoogle Scholar
  5. 5.
    Lam, W., Keung, C.K., Liu, D.: Discovering useful concept prototypes for classification based on filtering and abstraction. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(8), 1075–1090 (2002)CrossRefGoogle Scholar
  6. 6.
    Donamukkala, R., Huber, D., Kapuria, A., Hebert, M.: Automatic class selection and prototyping of 3-D object classification. In: Proc. 5th Int. Conf. on 3-D Digital Imaging and Modeling /3DIM 2005, pp. 64–71. IEEE, Los Alamitos (2005)CrossRefGoogle Scholar
  7. 7.
    Csákáky, P., Wallace, A.M.: Representation and classification of 3-D objects. IEEE Trans. on Systems, Man and Cybernetics - Part B: Cybern. 33(4), 638–647 (2003)CrossRefGoogle Scholar
  8. 8.
    Huber, D., Kapuria, A., Donamukkala, R., Hebert, M.: Part-based 3D object classification. In: Proc. IEEE Conf. on Computer Vision and pattern Recognition (CVPR 2004), vol. 2, pp. 82–89 (2004)Google Scholar
  9. 9.
    Zhang, J.: Selecting typical instances in instance-based learning. In: Proc. Int. conf. Machine Learning, pp. 470–479 (1992)Google Scholar
  10. 10.
    Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3D shape descriptors. In: Kobbelt, L., Schröder, P., Hoppe, H., (eds.) Proc. Symposium in Geometry Processing, pp. 156–165 (2003)Google Scholar
  11. 11.
    Chen, D., Ouhyoung, M., Tian, X., Shen, Y.: On visual similarity based 3D model retrieval. Computer Graphics Forum 22, 223–232 (2003)CrossRefGoogle Scholar
  12. 12.
    Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.L.: Topology matching for fully automatic similarity estimation of 3D shapes. In: Computer Graphics Proceedings, Annual Conference Series: SIGGRAPH Conference, pp. 203–212 (2001)Google Scholar
  13. 13.
    Biasotti, S., Marini, S.: 3D object comparison based on shape descriptors. International Journal of Computer Applications in Technology 23(2/3/4), 57–69 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. Biasotti
    • 1
  • D. Giorgi
    • 1
  • S. Marini
    • 1
  • M. Spagnuolo
    • 1
  • B. Falcidieno
    • 1
  1. 1.CNR-IMATIGenovaItaly

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