Advertisement

Model-Based 3D Object Localization Using Occluding Contours

  • Kenichi Maruyama
  • Yoshihiro Kawai
  • Fumiaki Tomita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)

Abstract

This paper describes a method for model-based 3D object localization. The object model consists of a triangular surface mesh, model points, and model geometrical features. Model points and model geometrical features are generated using contour generators, which are estimated by the occluding contours of projected images of the triangular surface mesh from multiple viewing directions, and they are maintained depending on the viewing direction. Multiple hypotheses for approximate model position and orientation are generated by comparing model geometrical features and data geometrical features. The multiple hypotheses are limited by using the viewing directions that are used to generate model geometrical features. Each hypothesis is verified and improved by using model points and 3D boundaries, which are reconstructed by segment-based stereo vision. In addition, each hypothesis is improved by using the triangular surface mesh and 3D boundaries. Experimental results show the effectiveness of the proposed method.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. on PAMI 21(5), 433–449 (1999)Google Scholar
  2. 2.
    Li, G., Tsin, Y., Genc, Y.: Exploiting occluding contours for real-time 3D tracking: A unified approach. In: Proc. ICCV 2007 (2007)Google Scholar
  3. 3.
    Drummond, T., Cipolla, R.: Real-time visual tracking of complex structures. IEEE Trans. on PAMI 24(7), 932–946 (2002)Google Scholar
  4. 4.
    Kotake, D., Satoh, K., Uchiyama, S., Yamamoto, H.: A fast initialization method for edge-based registration using an inclination constraint. In: Proc. ISMAR 2007, pp. 239–248 (2007)Google Scholar
  5. 5.
    Sethi, A., Renaudie, D., Kriegman, D., Ponce, J.: Curve and surface duals and the recognition of curved 3D objects from their silhouettes. Int. J. of Computer Vision 58(1), 73–86 (2004)CrossRefGoogle Scholar
  6. 6.
    Sumi, Y., Kawai, Y., Yoshimi, T., Tomita, F.: 3D object recognition in cluttered environments by segment-based stereo vision. Int. J. of Computer Vision 46(1), 5–23 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Sumi, Y., Ishiyama, Y., Tomita, F.: 3D localization of moving free-form objects in cluttered environments. In: Proc. ACCV 2004, vol. I, pp. 43–48 (2004)Google Scholar
  8. 8.
    Maruyama, K., Kawai, Y., Tomita, T.Y. F.: 3D object localization based on occluding contour using STL CAD model. In: Proc. 19th ICPR, TuBCT8.41 (2008)Google Scholar
  9. 9.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. on PAMI 14(2), 239–256 (1992)Google Scholar
  10. 10.
    Chen, Y., Medioni, G.: Object modeling by registration of multiple range image. Image and Vision Computing 10(3), 145–155 (1992)CrossRefGoogle Scholar
  11. 11.
    Vaillant, R., Faugeras, O.D.: Using external boundaries for 3-D object modeling. IEEE Trans. on PAMI 14(2), 157–173 (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kenichi Maruyama
    • 1
  • Yoshihiro Kawai
    • 1
  • Fumiaki Tomita
    • 1
  1. 1.National Institute of Advanced Industrial Science and TechnologyTsukuba, IbarakiJapan

Personalised recommendations