Exploiting Model Similarity for Indexing and Matching to a Large Model Database

  • Yi Tan
  • Bogdan C. Matei
  • Harpreet Sawhney
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)


This paper proposes a novel method to exploit model similarity in model-based 3D object recognition. The scenario consists of a large 3D model database of vehicles, and rapid indexing and matching needs to be done without sequential model alignment. In this scenario, the competition amongst shape features from similar models may pose serious challenge to recognition. To solve the problem, we propose to use a metric to quantitatively measure model similarities. For each model, we use similarity measures to define a model-centric class (MCC), which contains a group of similar models and the pose transformations between the model and its class members. Similarity information embedded in a MCC is used to boost matching hypotheses generation so that the correct model gains more opportunities to be hypothesized and identified. The algorithm is implemented and extensively tested on 1100 real LADAR scans of vehicles with a model database containing over 360 models.


Target Model Iterative Close Point Shape Signature Spin Image Model Database 
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.
    Lamdan, Y., Wolfson, H.: Geometric hashing: a general and efficient model-based recognition scheme. In: Proc. 2nd Intl. Conf. on Comp. Vision, pp. 238–249 (1988)Google Scholar
  2. 2.
    Gionis, Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of the 25th International Conference on Very Large Data Bases (VLDB 1999), pp. 518–529 (1999)Google Scholar
  3. 3.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI) 21(5), 433–449 (1999)CrossRefGoogle Scholar
  4. 4.
    Tangelder, J.W.H., Veltkamp, R.C.: A Survey of Content Based 3D Shape Retrieval Methods. In: IEEE International Conf. on Shape Modeling and Applications (2004)Google Scholar
  5. 5.
    Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., Jacobs, D.: A search engine for 3d models. ACM Transacions on Graphics, 83–105 (2003)Google Scholar
  6. 6.
    Besl, P., McKay, N.: A method for registration of 3D shapes. IEEE PAMI 14, 239–256 (1992)CrossRefGoogle Scholar
  7. 7.
    Belongie, S., Malik, J.: Matching with Shape Contexts. In: IEEE Workshop on Content-based access of Image and Video-Libraries (2000)Google Scholar
  8. 8.
    Mori, G., Belongie, S., Malik, H.: Shape contexts enable efficient retrieval of similar shapes. Computer Vision and Pattern Recognition 1, 723–730 (2001)Google Scholar
  9. 9.
    Veltkamp, R.C.: Shape matching: Similarity measures and algorithms. In: Shape Modeling International, May 2001, pp. 188–197 (2001)Google Scholar
  10. 10.
    Haralick, R.M., Joo, H., Lee, C.N., Zhuang, X., Vaidya, V.G., Kim, M.B.: Pose estimation from corresponding point data. IEEE Trans. on Systems, Man, and Cybernetics (August 1989)Google Scholar
  11. 11.
    Campbell, R., Flynn, P.: A survey of free-form object represent and recognition techniques. Computer Vision & Image Understanding 81(2), 166–210 (2001)CrossRefzbMATHGoogle Scholar
  12. 12.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conf. on Computer Vision (ICCV 1999), pp. 525–531 (1999)Google Scholar
  13. 13.
    Stein, F., Medioni, G.: Structural indexing: Efficient three dimensional object recognition. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI) 14(2), 125–145 (1992)CrossRefGoogle Scholar
  14. 14.
    Shan, Y., Matei, B., Sawhney, H.S., Kumar, R., Huber, D., Hebert, M.: Linear model hashing and batch RANSAC for rapid and accurate object recognition. In: IEEE Conf. on CVPR 2004 (2004)Google Scholar
  15. 15.
    Sharp, G.C., Lee, S.W., Wehe, D.K.: ICP Registration Using Invariant Features. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI) 24(1), 90–102 (2002)CrossRefGoogle Scholar
  16. 16.
    Huber, D., Kapuria, A., Donamukkala, R.R., Hebert, M.: Part-based 3D object classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2004) (June 2004)Google Scholar
  17. 17.
    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
  18. 18.
    Basri, R.: Recognition by Prototypes. International Journal of Computer Vision 19(2), 147–168 (1996)CrossRefGoogle Scholar
  19. 19.
    Matei, B.C., Sawhney, H.S., Spence, C.D.: Identification of highly similar 3D objects using model saliency. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 476–489. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yi Tan
    • 1
  • Bogdan C. Matei
    • 1
  • Harpreet Sawhney
    • 1
  1. 1.Sarnoff CorporationPrincetonUSA

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