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Object Recognition in 3D Point Cloud of Urban Street Scene

  • Pouria BabahajianiEmail author
  • Lixin Fan
  • Moncef Gabbouj
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9008)

Abstract

In this paper we present a novel street scene semantic recognition framework, which takes advantage of 3D point clouds captured by a high-definition LiDAR laser scanner. An important problem in object recognition is the need for sufficient labeled training data to learn robust classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by reduction of scene complexity using non-supervised ground and building segmentation. Our system first automatically segments grounds point cloud, this is because the ground connects almost all other objects and we will use a connect component based algorithm to oversegment the point clouds. Then, using binary range image processing building facades will be detected. Remained point cloud will grouped into voxels which are then transformed to super voxels. Local 3D features extracted from super voxels are classified by trained boosted decision trees and labeled with semantic classes e.g. tree, pedestrian, car, etc. The proposed method is evaluated both quantitatively and qualitatively on a challenging fixed-position Terrestrial Laser Scanning (TLS) Velodyne data set and two Mobile Laser Scanning (MLS), Paris-rue-Madam and NAVTEQ True databases. Robust scene parsing results are reported.

Keywords

Point Cloud Range Image Terrestrial Laser Scan Structure From Motion Ground Point 
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.

References

  1. 1.
    Liu, C., Yuen, J., Torralba, A.: Nonparametric scene parsing: label transfer via dense scene alignment. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009, CVPR 2009, pp. 1972–1979. IEEE (2009)Google Scholar
  2. 2.
    Csurka, G., Perronnin, F.: A simple high performance approach to semantic segmentation. In: BMVC, pp. 1–10 (2008)Google Scholar
  3. 3.
    Hoiem, D., Efros, A.A., Hebert, M.: Recovering surface layout from an image. Int. J. Comput. Vision 75, 151–172 (2007)CrossRefGoogle Scholar
  4. 4.
    Floros, G., Leibe, B.: Joint 2d–3d temporally consistent semantic segmentation of street scenes. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2823–2830. IEEE (2012)Google Scholar
  5. 5.
    Zhang, G., Jia, J., Wong, T.T., Bao, H.: Consistent depth maps recovery from a video sequence. IEEE Trans. Pattern Anal. Mach. Intell. 31, 974–988 (2009)CrossRefGoogle Scholar
  6. 6.
    Lu, W.L., Murphy, K.P., Little, J.J., Sheffer, A., Fu, H.: A hybrid conditional random field for estimating the underlying ground surface from airborne lidar data. IEEE Trans. Geosci. Remote Sens. 47, 2913–2922 (2009)CrossRefGoogle Scholar
  7. 7.
    Hernández, J., Marcotegui, B., et al.: Filtering of artifacts and pavement segmentation from mobile lidar data. In: ISPRS Workshop Laserscanning 2009 (2009)Google Scholar
  8. 8.
    Zhou, Y., Yu, Y., Lu, G., Du, S.: Super-segments based classification of 3d urban street scenes. Int. J. Adv. Rob. Syst. 9, 1–8 (2012)Google Scholar
  9. 9.
    Johnson, A.: Spin-Images: A Representation for 3-D Surface Matching. Ph.D. thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (1997)Google Scholar
  10. 10.
    Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3 d shape descriptors. In: Symposium on Geometry Processing, vol. 6 (2003)Google Scholar
  11. 11.
    Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Computer Graphics Forum, vol. 28, pp. 1383–1392. Wiley Online Library (2009)Google Scholar
  12. 12.
    Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape distributions. ACM Trans. Graph. (TOG) 21, 807–832 (2002)CrossRefGoogle Scholar
  13. 13.
    Knopp, J., Prasad, M., Van Gool, L.: Orientation invariant 3d object classification using hough transform based methods. In: Proceedings of the ACM Workshop on 3D Object Retrieval, pp. 15–20. ACM (2010)Google Scholar
  14. 14.
    Pavlidis, T.: Algorithms for Graphics and Image Processing. Computer Science Press, Rockville (1982)CrossRefGoogle Scholar
  15. 15.
    Klasing, K., Althoff, D., Wollherr, D., Buss, M.: Comparison of surface normal estimation methods for range sensing applications. In: IEEE International Conference on Robotics and Automation, 2009, ICRA 2009, pp. 3206–3211. IEEE (2009)Google Scholar
  16. 16.
    Zhang, C., Wang, L., Yang, R.: Semantic segmentation of urban scenes using dense depth maps. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 708–721. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  17. 17.
    Babahajiani, P., Fan, L., Gabbouj, M.: Semantic parsing of street scene images using 3d lidar point cloud. In: Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops, vol. 13, pp. 714–721 (2013)Google Scholar
  18. 18.
    Xiao, J., Quan, L.: Multiple view semantic segmentation for street view images. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 686–693. IEEE (2009)Google Scholar
  19. 19.
    Collins, M., Schapire, R.E., Singer, Y.: Logistic regression, adaboost and bregman distances. Mach. Learn. 48, 253–285 (2002)CrossRefzbMATHGoogle Scholar
  20. 20.
    Lai, K., Fox, D.: Object recognition in 3d point clouds using web data and domain adaptation. Int. J. Rob. Res. 29, 1019–1037 (2010)CrossRefGoogle Scholar
  21. 21.
    Serna, A., Marcotegui, B.: Attribute controlled reconstruction and adaptive mathematical morphology. In: Hendriks, C.L.L., Borgefors, G., Strand, R. (eds.) ISMM 2013. LNCS, vol. 7883, pp. 207–218. Springer, Heidelberg (2013) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Nokia Research CenterTampereFinland
  2. 2.Tampere University of TechnologyTampereFinland

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