Advertisement

3D Model Retrieval Using 2D View and Transform-Based Features

  • Pengjie Li
  • Huadong Ma
  • Anlong Ming
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

In this paper, we propose a novel hybrid shape descriptor that combines the 2D view and transform-based features. The 2D view features are extracted from six orthogonal directions of a 3D model by using the Scale Invariant Feature Transform (SIFT) method. In order to capture the six orthogonal 2D views, Continuous Principal Component Analysis (CPCA) is used to implement pose alignment. Meanwhile, the eigenspace is computed and stored to reduce the 2D view feature vector dimension. Then, the radial integral transform and the spherical integration transform are used to extract transform-based features. The similarity between the query model and models in the database is computed by using the weighted sum of 2D view and transform-based feature similarity. Experimental results show that the proposed hybrid shape descriptor can achieve satisfactory retrieval performance for both the articulated models in the McGill Shape Benchmark and the rigid models in the Princeton Shape Benchmark.

Keywords

3D model retrieval 2D view feature transform-based feature hybrid shape descriptor 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tangelder, J.W.H., Veltkamp, R.C.: A Survey of Content Based 3D Shape Retrieval Methods. Multimedia Tools and Applications 39(3), 441–571 (2008)CrossRefGoogle Scholar
  2. 2.
    Akgui, C.B., Sankur, B., Yemez, Y., Schmitt, F.: 3D Model Retrieval Using Probability Density-Based Shape Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(6), 1117–1133 (2009)CrossRefGoogle Scholar
  3. 3.
    Chen, D.Y.: On visual similarity based 3D model retrieval. Computer Graphics Forum 22(3), 223–232 (2003)CrossRefGoogle Scholar
  4. 4.
    Shilane, P.: Three-dimensional shape search: state-of-the-art review and future trends. Computer-Aided Design 37(7), 509–530 (2005)Google Scholar
  5. 5.
    Vranic, D.V.: Desire: a composite 3d-shape descriptor. In: Proc. of IEEE Conference on Multimedia and Expo., Lausanne, Switzerland, pp. 425–428 (2002)Google Scholar
  6. 6.
    Papadakis, P., Pratikakis, I., Theoharis, T., Passalis, G., Perantonis, S.: 3D Object Retrieval using an Efficient and Compact Hybrid Shape Descriptor. In: Eurographics Workshop on 3D Object Retrieval (2008)Google Scholar
  7. 7.
    Shih, J.-L., Chen, H.-Y.: A 3D model retrieval approach using the interior and exterior 3D shape information. Multimedia Tools Appl. 43, 45–62 (2009)CrossRefGoogle Scholar
  8. 8.
    Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient local visual featuers for shape- based 3D model retrieval. In: Proceedings of Shape Modeling International, pp. 93–102 (2008)Google Scholar
  9. 9.
    Ohbuchi, R., Furuya, T.: Accelerating Bag-of-Features SIFT Algorithm for 3D Model Retrieval. In: Proc. SAMT 2008 Workshop on Semantic 3D Media (S-3D), pp. 23–30 (2008)Google Scholar
  10. 10.
    Zhu, K.P., Wong, Y.S., Lu, W.F., Loh, H.T.: 3D CAD model matching from 2D local invariant features. Computers in industry (2009)Google Scholar
  11. 11.
    Daras, P., Zarpalas, D., Tzovaras, D., Strintzis, M.G.: Efficient 3-D model search and retrieval using generalized 3-D radon transforms. IEEE Trans. Multimedia 8(1), 101–114 (2006)CrossRefGoogle Scholar
  12. 12.
    Schurmans, U., Razdan, A., Simon, A., Mccartney, P., Marzke, M., Alfen, D.V.: Advances in geometric modeling and feature extraction on pots, rocks and bones for representation and query via the internet. Comput. Applicat. Archaeol., 25–29 (2001)Google Scholar
  13. 13.
    Vranic, D.V.: 3D model retrieval. Ph D dissertation. University of Leipzig, Leipzig (2004)Google Scholar
  14. 14.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Ke, R.S.: PCA-SIFT: A more distinctive representation for local image descriptors. In: Proc. CVPR, vol. 2, pp. 506–513 (2004)Google Scholar
  16. 16.
    Toft, P.: The Radon Transform: Theory and Implementation. Techn. Univ. Denmark, Lyngby (1996)Google Scholar
  17. 17.
    Zhang, J., Kaplow, R., Chen, R., Siddiqi, K.: The McGill Shape Benchmark (2005)Google Scholar
  18. 18.
    Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton Shape Benchmark. In: Proc. SMI, pp. 167–178 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pengjie Li
    • 1
  • Huadong Ma
    • 1
  • Anlong Ming
    • 1
  1. 1.Beijing Key Laboratory of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations