Object Recognition Using Constraints from Primitive Shape Matching

  • Nikhil Somani
  • Caixia Cai
  • Alexander Perzylo
  • Markus Rickert
  • Alois Knoll
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8887)

Abstract

In this paper, an object recognition and pose estimation approach based on constraints from primitive shape matching is presented. Additionally, an approach for primitive shape detection from point clouds using an energy minimization formulation is presented. Each primitive shape in an object adds geometric constraints on the object’s pose. An algorithm is proposed to find minimal sets of primitive shapes which are sufficient to determine the complete 3D position and orientation of a rigid object. The pose is estimated using a linear least squares solver over the combination of constraints enforced by the primitive shapes. Experiments illustrating the primitive shape decomposition of object models, detection of these minimal sets, feature vector calculation for sets of shapes and object pose estimation have been presented on simulated and real data.

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References

  1. 1.
    Hu, G.: 3-d object matching in the hough space. In: IEEE International Conference on Systems, Man and Cybernetics, Intelligent Systems for the 21st Century, vol. 3, pp. 2718–2723 (1995)Google Scholar
  2. 2.
    Somani, N., Dean, E., Cai, C., Knoll, A.: Scene perception and recognition in industrial environments for human-robot interaction. In: Proceedings of the 9th International Symposium on Visual Computing (2013)Google Scholar
  3. 3.
    Schnabel, R., Wessel, R., Wahl, R., Klein, R.: Shape recognition in 3d point-clouds. In: Skala, V. (ed.) The 16th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2008. UNION Agency-Science Press (2008)Google Scholar
  4. 4.
    Papazov, C., Haddadin, S., Parusel, S., Krieger, K., Burschka, D.: Rigid 3D geometry matching for grasping of known objects in cluttered scenes. International Journal of Robotic Research 31, 538–553 (2012)CrossRefGoogle Scholar
  5. 5.
    Papazov, C., Burschka, D.: An efficient ransac for 3d object recognition in noisy and occluded scenes. In: Proceedings of the 10th Asian Conference on Computer Vision, Part I, pp. 135–148 (2011)Google Scholar
  6. 6.
    Thomas, U.: Stable pose estimation using ransac with triple point feature hash maps and symmetry exploration. In: MVA, pp. 109–112 (2013)Google Scholar
  7. 7.
    Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition. In: Proceedings of the 15th International Conference on Multimedia, MULTIMEDIA 2007, pp. 357–360. ACM, New York (2007)Google Scholar
  8. 8.
    Sipiran, I., Bustos, B.: Harris 3d: a robust extension of the harris operator for interest point detection on 3d meshes. Vis. Comput. 27, 963–976 (2011)CrossRefGoogle Scholar
  9. 9.
    Zhong, Y.: Intrinsic shape signatures: A shape descriptor for 3d object recognition. In: Computer Vision Workshops (ICCV Workshops), pp. 689–696 (2009)Google Scholar
  10. 10.
    Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3d recognition and pose using the viewpoint feature histogram. In: 2010 Intelligent Robots and Systems (IROS), pp. 2155–2162. IEEE (2010)Google Scholar
  11. 11.
    Biegelbauer, G., Vincze, M.: Efficient 3d object detection by fitting superquadrics to range image data for robot’s object manipulation. In: 2007 IEEE International Conference on Robotics and Automation, pp. 1086–1091 (2007)Google Scholar
  12. 12.
    Holz, D., Holzer, S., Rusu, R.B., Behnke, S.: Real-time plane segmentation using RGB-D cameras. In: Röfer, T., Mayer, N.M., Savage, J., Saranlı, U. (eds.) RoboCup 2011. LNCS, vol. 7416, pp. 306–317. Springer, Heidelberg (2012)Google Scholar
  13. 13.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)CrossRefGoogle Scholar
  14. 14.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  15. 15.
    Delong, A., Osokin, A., Isack, H., Boykov, Y.: Fast approximate energy minimization with label costs. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2173–2180 (2010)Google Scholar
  16. 16.
    Delong, A., Osokin, A., Isack, H.N., Boykov, Y.: Fast approximate energy minimization with label costs. Int. J. Comput. Vision 96, 1–27 (2012)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Richtsfeld, A., Morwald, T., Prankl, J., Zillich, M., Vincze, M.: Segmentation of unknown objects in indoor environments. In: 2012 Intelligent Robots and Systems (IROS), pp. 4791–4796 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nikhil Somani
    • 1
  • Caixia Cai
    • 2
  • Alexander Perzylo
    • 1
  • Markus Rickert
    • 1
  • Alois Knoll
    • 2
  1. 1.Cyber-Physical Systemsfortiss - An-Institut der Technischen Universität MünchenMünchenGermany
  2. 2.Fakultät für InformatikTechnische Universität MünchenGarching bei MünchenGermany

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