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Efficient Object Categorization with the Surface-Approximation Polynomials Descriptor

  • Richard Bormann
  • Jan Fischer
  • Georg Arbeiter
  • Alexander Verl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7463)

Abstract

Perception of object categories is a key functionality towards more versatile autonomous robots. Object categorization enables robots to understand their environments even if certain instances of objects have never been seen before. In this paper we present the novel descriptor Surface-Approximation Polynomials (SAP) that directly computes a global description on point cloud surfaces of objects based on polynomial approximations of surface cuts. This descriptor is directly applicable to point clouds captured with time-of-flight or other depth sensors without any data preprocessing or normal computation. Hence, it is generated very fast. Together with a preceding pose normalization, SAP is invariant to scale and partially invariant to rotations. We demonstrate experiments in which SAP categorizes 78 % of test objects correctly while needing only 57 ms for the computation. This way SAP is superior to GFPFH, GRSD and VFH according to both criteria.

Keywords

Object Categorization Robot Vision 3D Descriptor 

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References

  1. 1.
    Rusu, R.B., Holzbach, A., Beetz, M., Bradski, G.: Detecting and segmenting objects for mobile manipulation. In: ICCV, S3DV Workshop (2009)Google Scholar
  2. 2.
    Marton, Z.C., Pangercic, D., Blodow, N., Beetz, M.: Combined 2D-3D Categorization and Classification for Multimodal Perception Systems. The International Journal of Robotics Research 30(11), 1378–1402 (2011)CrossRefGoogle Scholar
  3. 3.
    Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3D recognition and pose using the viewpoint feature histogram. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan (2010)Google Scholar
  4. 4.
    Galleguillos, C., Belongie, S.: Context based object categorization: A critical survey. Computer Vision and Image Understanding (CVIU) 114, 712–722 (2010)CrossRefGoogle Scholar
  5. 5.
    Zhang, H., Berg, A.C., Maire, M., Malik, J.: SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2126–2136 (2006)Google Scholar
  6. 6.
    Deng, J., Berg, A.C., Li, K., Fei-Fei, L.: What Does Classifying More Than 10,000 Image Categories Tell Us? In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 71–84. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans. PAMI 21(1), 433–449 (1999)CrossRefGoogle Scholar
  8. 8.
    Koenderink, J.J., van Doorn, A.J.: Surface shape and curvature scales. Image Vision Computing 10, 557–565 (1992)CrossRefGoogle Scholar
  9. 9.
    Knopp, J., Prasad, M., Willems, G., Timofte, R., Van Gool, L.: Hough Transform and 3D SURF for Robust Three Dimensional Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 589–602. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Tombari, F., Salti, S., Di Stefano, L.: Unique Signatures of Histograms for Local Surface Description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Knopp, J., Prasad, M., Van Gool, L.: Orientation invariant 3D object classification using hough transform based methods. In: Proc. of the ACM Workshop on 3D Object Retrieval, pp. 15–20 (2010)Google Scholar
  12. 12.
    Toldo, R., Castellani, U., Fusiello, A.: A bag of words approach for 3D object categorization. In: Proc. of Int. Conference on Computer Vision, pp. 116–127 (2009)Google Scholar
  13. 13.
    Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape distributions. ACM Tr. on Graphics 21(4), 807–832 (2002)CrossRefGoogle Scholar
  14. 14.
    Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3D shape descriptors. In: Symposium on Geometry Processing (June 2003)Google Scholar
  15. 15.
    Pu, J., Yi, L., Guyu, X., Hongbin, Z., Weibin, L., Uehara, Y.: 3D model retrieval based on 2D slice similarity measurements. In: Proceedings of the 3D Data Processing, Visualization, and Transmission, pp. 95–101 (2004)Google Scholar
  16. 16.
    Endres, F., Plagemann, C., Stachniss, C., Burgard, W.: Unsupervised discovery of object classes from range data using latent dirichlet allocation. In: Proc. of Robotics: Science and Systems (2009)Google Scholar
  17. 17.
    Bo, L., Ren, X., Fox, D.: Depth Kernel Descriptors for Object Recognition. In: IROS (September 2011)Google Scholar
  18. 18.
    Wahl, E., Hillenbrand, U., Hirzinger, G.: Surflet-pair-relation histograms: A statistical 3D-shape representation for rapid classification. In: 3-D Digital Imaging and Modeling, pp. 474–481 (2003)Google Scholar
  19. 19.
    Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: Proc. of Int. Conference on Robotics and Automation (ICRA), Shanghai, China (2011)Google Scholar
  20. 20.
    Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Marton, Z.C., Rusu, R.B., Jain, D., Klank, U., Beetz, M.: Probabilistic Categorization of Kitchen Objects in Table Settings with a Composite Sensor. In: Proc. of the Int. Conf. on Intelligent Robots and Systems, St. Louis, MO, USA (2009)Google Scholar
  22. 22.
    Collet Romea, A., Srinivasa, S., Hebert, M.: Structure discovery in multi-modal data: a region-based approach. In: Proceedings of ICRA (2011)Google Scholar
  23. 23.
    Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)zbMATHCrossRefGoogle Scholar
  24. 24.
    Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  25. 25.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annals of Statistics 28, 2000 (1998)MathSciNetGoogle Scholar
  26. 26.
    Mozos, O.M., Burgard, W.: Supervised learning of topological maps using semantic information extracted from range data. In: IROS, pp. 2772–2777 (2006)Google Scholar
  27. 27.
    Browatzki, B., Fischer, J., Graf, B., Bülthoff, H., Wallraven, C.: Going into depth: Evaluating 2D and 3D cues for object classification on a new, large-scale object dataset. In: Proc. of Int. Conf. Computer Vision Workshop on CD4CV, pp. 1–7 (2011)Google Scholar
  28. 28.
    Bradski, G., Kaehler, A.: Learning opencv: Computer vision with the opencv library (2008)Google Scholar
  29. 29.
    Chang, C., Lin, C.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Richard Bormann
    • 1
  • Jan Fischer
    • 1
  • Georg Arbeiter
    • 1
  • Alexander Verl
    • 1
  1. 1.Fraunhofer IPAStuttgartGermany

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