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Graspable Parts Recognition in Man-Made 3D Shapes

  • Hamid Laga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7725)

Abstract

We address the problem of automatic recognition of graspable parts in man-made 3D shapes, which exhibit high intra-class variability that cannot be captured with geometric descriptors alone. We observe that, in the presence of significant geometric and topological variations, the context of a part within a 3D shape provides important cues about its functionality. We propose to model the context as structural relationships between shape parts and use them, in addition to part geometry, as cues for identifying automatically the graspable parts. We design a set of spatial relationships that can be extracted from general shapes. Then, we propose a new similarity measure that captures a part context and enables better recognition of graspable parts. We use this property to design a classifier that learns the semantics of a shape part. We demonstrate that our approach outperforms the state-of-the-art approaches that are purely geometric-based.

Keywords

Iterative Close Point Geometric Descriptor Shape Part Contextual Relationship Topological Variation 
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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References

  1. 1.
    Grabner, H., Gall, J., Van Gool, L.: What makes a chair a chair? In: CVPR, pp. 1529–1536 (2011)Google Scholar
  2. 2.
    Sahbani, A., El-Khoury, S., Bidaud, P.: An overview of 3D object grasp synthesis algorithms. Robotics and Autonomous Systems 60 (3), 326–336 (2012)CrossRefGoogle Scholar
  3. 3.
    Oliva, A., Torralba, A.: The role of context in object recognition. Trends in Cogn. Sciences 11, 520–527 (2007)CrossRefGoogle Scholar
  4. 4.
    Fisher, M., Savva, M., Hanrahan, P.: Characterizing structural relationships in scenes using graph kernels. ACM Trans. Graph. 30, 34:1–34:12 (2011)CrossRefGoogle Scholar
  5. 5.
    Gupta, A., Davis, L.: Objects in action: An approach for combining action understanding and object perception. In: CVPR, pp. 1–8 (2007)Google Scholar
  6. 6.
    Stark, M., Lies, P., Zillich, M., Wyatt, J., Schiele, B.: Functional object class detection based on learned affordance cues. In: ICVS, pp. 435–444 (2008)Google Scholar
  7. 7.
    Aksoy, E., Abramov, A., Wörgötter, F., Dellen, B.: Categorizing object-action relations from semantic scene graphs. In: ICRA, pp. 398–405 (2010)Google Scholar
  8. 8.
    Kjellström, H., Romero, J., Kragić, D.: Visual object-action recognition: Inferring object affordances from human demonstration. CVIU 115, 81–90 (2011)Google Scholar
  9. 9.
    Huebner, K., Ruthotto, S., Kragic, D.: Minimum volume bounding box decomposition for shape approximation in robot grasping. In: ICRA, pp. 1628–1633 (2008)Google Scholar
  10. 10.
    Kyota, F., Watabe, T., Saito, S., Nakajima, M.: Detection and evaluation of grasping positions for autonomous agents. In: CW, pp. 453–460 (2005)Google Scholar
  11. 11.
    Miller, A., Knoop, S., Christensen, H., Allen, P.: Automatic grasp planning using shape primitives. In: ICRA, vol. 2, pp. 1824–1829 (2003)Google Scholar
  12. 12.
    Goldfeder, C., Allen, P., Lackner, C., Pelossof, R.: Grasp planning via decomposition trees. In: ICRA, pp. 4679–4684 (2007)Google Scholar
  13. 13.
    Przybylski, M., Asfour, T., Dillmann, R.: Unions of balls for shape approximation in robot grasping. In: IROS, pp. 1592–1599 (2010)Google Scholar
  14. 14.
    Aleotti, J., Caselli, S.: A 3D shape segmentation approach for robot grasping by parts. Robotics and Autonomous Systems 60, 358–366 (2012)CrossRefGoogle Scholar
  15. 15.
    El-Khoury, S., Sahbani, A., Perdereau, V.: Learning the natural grasping component of an unknown object. In: IROS, pp. 2957–2962 (2007)Google Scholar
  16. 16.
    Sahbani, A., El-Khoury, S.: A hybrid approach for grasping 3D objects. In: IROS, pp. 1272–1277 (2009)Google Scholar
  17. 17.
    Bohg, J., Kragic, D.: Grasping familiar objects using shape context. In: Int. Conf. on Advanced Robotics (2009)Google Scholar
  18. 18.
    Galleguillos, C., Belongie, S.J.: Context based object categorization: A critical survey. Computer Vision and Image Understanding 114, 712–722 (2010)CrossRefGoogle Scholar
  19. 19.
    Strat, T.M., Fischler, M.A.: Context-based vision: Recognizing objects using information from both 2d and 3D imagery. PAMI 13, 1050–1065 (1991)CrossRefGoogle Scholar
  20. 20.
    Galleguillos, C., Rabinovich, A., Belongie, S.: Object categorization using co-occurrence, location and appearance. In: CVPR (2008)Google Scholar
  21. 21.
    Popovic, M., Kootstra, G., Jørgensen, J.A., Kragic, D., Krüger, N.: Grasping unknown objects using an early cognitive vision system for general scene understanding. In: IROS, pp. 987–994 (2011)Google Scholar
  22. 22.
    Luo, G., Bergström, N., Ek, C.H., Kragic, D.: Representing actions with kernels. In: IROS, pp. 2028–2035 (2011)Google Scholar
  23. 23.
    Li, Y., Fu, J.L., Pollard, N.S.: Data-driven grasp synthesis using shape matching and task-based pruning. IEEE Trans. on Vis. and CG 13, 732–747 (2007)CrossRefGoogle Scholar
  24. 24.
    Saxena, A., Driemeyer, J., Ng, A.Y.: Robotic grasping of novel objects using vision. Int. J. Rob. Res. 27, 157–173 (2008)CrossRefGoogle Scholar
  25. 25.
    Saxena, A., Driemeyer, J., Kearns, J., Osondu, C., Ng, A.Y.: Learning to grasp novel objects using vision. In: Int. Symp. of Experimental Robotics (2006)Google Scholar
  26. 26.
    Montesano, L., Lopes, M.: Learning grasping affordances from local visual descriptors. In: IEEE International Conference on Development and Learning, pp. 1–6 (2009)Google Scholar
  27. 27.
    Detry, R., Baseski, E., Popovic, M., Touati, Y., Kruger, N., Kroemer, O., Peters, J., Piater, J.: Learning object-specific grasp affordance densities. In: IEEE International Conference on Development and Learning, pp. 1–7 (2009)Google Scholar
  28. 28.
    Klingbeil, E., Rao, D., Carpenter, B., Ganapathi, V., Ng, A.Y., Khatib, O.: Grasping with application to an autonomous checkout robot. In: ICRA, pp. 2837–2844 (2011)Google Scholar
  29. 29.
    Harchaoui, Z., Bach, F.: Image classification with segmentation graphs. In: CVPR (2007)Google Scholar
  30. 30.
    Fisher, M., Hanrahan, P.: Context-based search for 3D models. ACM Trans. Graph. 29, 182:1–182:10 (2010)Google Scholar
  31. 31.
    Kalogerakis, E., Hertzmann, A., Singh, K.: Learning 3D mesh segmentation and labeling. ACM Trans. Graph. 29, 102:1–102:12 (2010)Google Scholar
  32. 32.
    Chen, X., Golovinskiy, A., Funkhouser, T.: A benchmark for 3D mesh segmentation. ACM Trans. Graph. 28, 73:1–73:12 (2009)Google Scholar
  33. 33.
    Wang, Y., Xu, K., Li, J., Zhang, H., Shamir, A., Liu, L., Cheng, Z.Q., Xiong, Y.: Symmetry hierarchy of man-made objects. Comput. Graph. Forum 30, 287–296 (2011)CrossRefGoogle Scholar
  34. 34.
    Osada, R., Funkhouser, T.A., Chazelle, B., Dobkin, D.P.: Shape distributions. ACM Transactions on Graphics 21, 807–832 (2002)CrossRefGoogle Scholar
  35. 35.
    Laga, H., Takahashi, H., Nakajima, M.: Spherical wavelet descriptors for content-based 3D model retrieval. In: IEEE SMI, p. 15 (2006)Google Scholar
  36. 36.
    Chaudhuri, S., Kalogerakis, E., Guibas, L., Koltun, V.: Probabilistic reasoning for assembly-based 3D modeling. ACM Transactions on Graphic 30, 35:1–35:10 (2011)Google Scholar
  37. 37.
    Zheng, Y., Fu, H., Cohen-Or, D., Au, O.K.C., Tai, C.L.: Component-wise controllers for structure-preserving shape manipulation. Computer Graphics Forum 30, 563–572 (2011)CrossRefGoogle Scholar
  38. 38.
    Malisiewicz, T., Efros, A.: Beyond categories: The visual memex model for reasoning about object relationships. In: Neural Information Processing Systems (2009)Google Scholar
  39. 39.
    van Kaick, O., Tagliasacchi, A., Sidi, O., Zhang, H., Cohen-Or, D., Wolf, L., Hamarneh, G.: Prior knowledge for shape correspondence. Computer Graphics Forum 30, 553–562 (2011)CrossRefGoogle Scholar
  40. 40.
    Schnabel, R., Wahl, R., Klein, R.: Efficient ransac for point-cloud shape detection. Comput. Graph. Forum 26, 214–226 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hamid Laga
    • 1
  1. 1.Phenomics and Bioinformatics Research CentreUniversity of South AustraliaAustralia

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