Identification of Highly Similar 3D Objects Using Model Saliency

  • Bogdan C. Matei
  • Harpreet S. Sawhney
  • Clay D. Spence
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3954)


We present a novel approach for identifying 3D objects from a database of models, highly similar in shape, using range data acquired in unconstrained settings from a limited number of viewing directions. We are addressing also the challenging case of identifying targets not present in the database. The method is based on learning offline saliency tests for each object in the database, by maximizing an objective measure of discriminability with respect to other similar models. Our notion of model saliency differs from traditionally used structural saliency that characterizes weakly the uniqueness of a region by the amount of 3D texture available, by directly linking discriminability with the Bhattacharyya distance between the distribution of errors between the target and its corresponding ground truth, respectively other similar models. Our approach was evaluated on thousands of queries obtained by different sensors and acquired in various operating conditions and using a database of hundreds of models. The results presented show a significant improvement in the recognition performance when using saliency compared to global point-to-point mismatch errors, traditionally used in matching and verification algorithms.


Point Cloud Salient Region Range Sensor Model Saliency Iterate Close Point Algorithm 
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.
    Arun, K., Huang, T., Blostein, S.: Least-squares fitting of two 3D point sets. PAMI 9, 698–700 (1987)CrossRefGoogle Scholar
  2. 2.
    Besl, P., McKay, N.: A method for registration of 3d shapes. PAMI 18, 540–547 (1992)Google Scholar
  3. 3.
    Chen, Y., Medioni, G.: Object modeling by registration of multiple range images. In: IVC, pp. 145–155 (1992)Google Scholar
  4. 4.
    Comaniciu, D., Visvanath, R., Meer, P.: Kernel-based object tracking. PAMI 25(5), 564–577 (2003)CrossRefGoogle Scholar
  5. 5.
    Domeniconi, C., Peng, J., Gunopulos, D.: Ocally adaptive metric nearest neighbor classification. PAMI 24(9), 1281–1285 (2002)CrossRefGoogle Scholar
  6. 6.
    Efron, B., Tibshirani, R.: An Introduction to the Bootstrap. Chapman & Hall, New York (1993)CrossRefMATHGoogle Scholar
  7. 7.
    Ferencz, A., Learned-Miller, E.G., Malik, J.: Learning hyper-features for visual identification. In: NIPS (2004)Google Scholar
  8. 8.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)MATHGoogle Scholar
  9. 9.
    Gelfand, N., Ikemoto, L., Rusinkiewicz, S., Levoy, M.: Geometrically stable sampling for the ICP algorithm. In: 3DIM 2003 (2003)Google Scholar
  10. 10.
    Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning. Data mining inference and prediction. Springer, Heidelberg (2001)MATHGoogle Scholar
  11. 11.
    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. In: ACM SIGGRAPH, pp. 71–78 (1992)Google Scholar
  12. 12.
    Huber, D., Kapuria, A., Donamukkala, R., Hebert, M.: Part-based 3D object classification. In: CVPR, Washington, DC, June 2004, vol. 2, pp. 82–89 (2004)Google Scholar
  13. 13.
    Johnson, A., Hebert, M.: Surface matching for object recognition in complex three-dimensional scenes. IVC 16, 635–651 (1998)CrossRefGoogle Scholar
  14. 14.
    Kadir, T., Zisermann, A.: Scale, saliency and image description. Commun. Assoc. Comp. Mach. 24, 381–395 (1981)Google Scholar
  15. 15.
    Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. In: ACM SIGGRAPH, pp. 659–666 (2005)Google Scholar
  16. 16.
    Neisser, U.: Visual search. Scientific American 20(12), 94–102 (1964)CrossRefGoogle Scholar
  17. 17.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the icp algorithm. In: Third International Conference on 3D Digital Imaging and Modeling (3DIM 2001) (2001)Google Scholar
  18. 18.
    Wasserman, L.: All of statistics. A concise course in statistical inferrence. Springer, Heidelberg (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bogdan C. Matei
    • 1
  • Harpreet S. Sawhney
    • 1
  • Clay D. Spence
    • 1
  1. 1.Sarnoff CorporationPrincetonUSA

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