International Conference on Advanced Concepts for Intelligent Vision Systems

Advanced Concepts for Intelligent Vision Systems pp 530-540 | Cite as

Plane Extraction for Indoor Place Recognition

  • Ciro Potena
  • Alberto Pretto
  • Domenico D. Bloisi
  • Daniele Nardi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9386)


In this paper, we present an image based plane extraction method well suited for real-time operations. Our approach exploits the assumption that the surrounding scene is mainly composed by planes disposed in known directions. Planes are detected from a single image exploiting a voting scheme that takes into account the vanishing lines. Then, candidate planes are validated and merged using a region growing based approach to detect in real-time planes inside an unknown indoor environment. Using the related plane homographies is possible to remove the perspective distortion, enabling standard place recognition algorithms to work in an invariant point of view setup. Quantitative Experiments performed with real world images show the effectiveness of our approach compared with a very popular method.


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  1. 1.
    Baker, S., Szeliski, R., Anandan, P.: A layered approach to stereo reconstruction. In: CVPR, pp. 434–441 (1998)Google Scholar
  2. 2.
    Canny, J.: A computational approach to edge detection 8, 679–697 (1986)Google Scholar
  3. 3.
    Coughlan, J., Yuille, A.: Manhattan world: compass direction from a single image by bayesian inference. In: CVPR, vol. 2, pp. 941–947 (1999)Google Scholar
  4. 4.
    Cummins, M., Newman, P.: Appearance-only SLAM at large scale with FAB-MAP 2.0. The International Journal of Robotics Research (2010)Google Scholar
  5. 5.
    Denis, P., Elder, J.H., Estrada, F.J.: Efficient edge-based methods for estimating manhattan frames in urban imagery. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 197–210. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  6. 6.
    Flint, A., Mei, C., Murray, D., Reid, I.: A dynamic programming approach to reconstructing building interiors. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 394–407. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  7. 7.
    Flint, A., Murray, D., Reid, I.: Manhattan scene understanding using monocular, stereo, and 3d features. In: ICCV, pp. 2228–2235, November 2011Google Scholar
  8. 8.
    Furukawa, Y., Curless, B., Seitz, S., Szeliski, R.: Manhattan-world stereo. In: CVPR (2009)Google Scholar
  9. 9.
    Galvez-Lopez, D., Tardos, J.: Real-time loop detection with bags of binary words. In: IROS, pp. 51–58, September 2011Google Scholar
  10. 10.
    Guan, L., Yu, T., Tu, P., Lim, S.N.: Simultaneous image segmentation and 3d plane fitting for rgb-d sensors — an iterative framework. In: CVPR Workshops, pp. 49–56 (2012)Google Scholar
  11. 11.
    Hoiem, D., Efros, A., Hebert, M.: Geometric context from a single image. In: ICCV, vol. 1, pp. 654–661 (2005)Google Scholar
  12. 12.
    Labbe, M., Michaud, F.: Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Transactions on Robotics 29(3), 734–745 (2013)CrossRefGoogle Scholar
  13. 13.
    Lee, D., Hebert, M., Kanade, T.: Geometric reasoning for single image structure recovery. In: CVPR, pp. 2136–2143 (2009)Google Scholar
  14. 14.
    Matas, J., Galambos, C., Kittler, J.: Robust detection of lines using the progressive probabilistic hough transform. In: CVIU, April 2000Google Scholar
  15. 15.
    Rother, C.: A new approach for vanishing point detection in architectural environments. In: BMVC, pp. 382–391 (2000)Google Scholar
  16. 16.
    Saxena, A., Sun, M., Ng, A.: Make3d: learning 3d scene structure from a single still image. In: PAMI, pp. 824–840 (2009)Google Scholar
  17. 17.
    Silberman, N., Fergus, R.: Indoor scene segmentation using a structured light sensor. In: ICCV Workshops, pp. 601–608 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ciro Potena
    • 1
  • Alberto Pretto
    • 1
  • Domenico D. Bloisi
    • 1
  • Daniele Nardi
    • 1
  1. 1.Department of Computer, Control and Management EngineeringSapienza University of RomeRomeItaly

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