Planar Surfaces Recognition in 3D Point Cloud Using a Real-Coded Multistage Genetic Algorithm

  • Mosab Bazargani
  • Luís Mateus
  • Maria Amélia R. Loja
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9028)

Abstract

Most frequent surface shapes of man-made constructions are planar surfaces. Discovering those surfaces is a big step toward extracting as-built/-is construction information from 3D point cloud. In this paper, a real-coded genetic algorithm (GA) formulation for planar surfaces recognition in 3D point clouds is presented. The algorithm developed based on a multistage approach; thereby, it finds one planar surface (part of solution) at each stage. In addition, the logarithmically proportional objective function that is used in this approach can adapt itself to scale and spatial density of the point cloud. We tested the proposed application on a synthetic point cloud containing several planar surfaces with different shapes, positions, and with a wide variety of sizes. The results obtained showed that the proposed method is capable to find all plane’s configurations of flat surfaces with a minor distance to the actual configurations.

Keywords

Planar surface recognition Multistage genetic algorithm Logarithmic objective function Point cloud 

References

  1. 1.
    Tang, P., Anil, E., Akinci, B., Huber, D.: Efficient and effective quality assessment of as-is building information models and 3D laser-scanned data. In: Proceedings of ASCE International Workshop on Computing in Civil Engineering, pp. 486–493. McGraw-Hill (2011)Google Scholar
  2. 2.
    Tang, P., Huber, D., Akinci, B., Lipman, R., Lytle, A.: Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques. Autom. Constr. 19, 829–843 (2010)CrossRefGoogle Scholar
  3. 3.
    Jiang, X., Bunke, H.: Robust and fast edge detection and description in range images. In: Proceedings of IAPR Workshop on Machine Vision Applications, pp. 538–541 (1996)Google Scholar
  4. 4.
    Vosselman, G., Gorte, B., Sithole, G., Rabbani, T.: Recognising structure in laser-scanner point clouds. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVI-8/W2, pp. 33–38 (2004)Google Scholar
  5. 5.
    Gorte, B.: Segmentation of TIN-structured surface models. In: Proceedings Joint International Symposium on Geospatial Theory, Processing and Applications (2002)Google Scholar
  6. 6.
    He, Y., Zhang, C., Awrangjeb, M., Fraser, C.: Automated reconstruction of walls from airborne lidar data for complete 3D building modelling. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXIX-B3, pp. 115–120 (2012)Google Scholar
  7. 7.
    Tarsha-Kurdi, F., Landes, T., Grussenmeyer, P.: Hough-transform and extended RANSAC algorithms for automatic detection of 3D building roof planes from lidar data. In: ISPRS Workshop on Laser Scanning, vol. XXXVI (2007)Google Scholar
  8. 8.
    Peternell, M., Steiner, T.: Reconstruction of piecewise planar objects from point clouds. Comput. Aided Des. 36, 333–342 (2004)CrossRefGoogle Scholar
  9. 9.
    Sanchez, V., Zakhor, A.: Planar 3D modeling of building interiors from point cloud data. In: 19th IEEE International Conference on Image Processing (ICIP), pp. 1777–1780 (2012)Google Scholar
  10. 10.
    Ozog, P., Eustice, R.M.: Real-time SLAM with piecewise-planar surface models and sparse 3D point clouds. In: Proceedings of the IEEE/RSJ International Conference on Intelligent RObots and Systems (IROS), pp. 1042–1049 (2013)Google Scholar
  11. 11.
    Jenke, P., Huhle, B., Straßer, W.: Statistical reconstruction of indoor scenes. In: International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG 2009), pp. 17–24 (2009)Google Scholar
  12. 12.
    Chen, J., Chen, B.: Architectural modeling from sparsely scanned range data. Int. J. Comput. Vis. 78, 223–236 (2008)CrossRefGoogle Scholar
  13. 13.
    Vosselman, G., Dijkman, S.: 3D building model reconstruction from point clouds and ground plans. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXIV-3/W4, pp. 37–43 (2001)Google Scholar
  14. 14.
    Yousefzadeh, M., Leurink, F.H.M., Beheshti Jou, M.: A general data-driven algorithm for façade structure modeling using ground based laser data. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-3, pp. 381–386 (2014)Google Scholar
  15. 15.
    Mahfoud, S.W.: A comparison of parallel and sequential niching methods. In: Eshelman, L.J. (ed.) Proceedings of the 6th International Conference on Genetic Algorithms, pp. 136–143. Morgan Kaufmann, Pittsburgh (1995)Google Scholar
  16. 16.
    Horn, J.: The nature of niching: Genetic algorithms and the evolution of optimal, cooperative populations. Technical report IlliGAL No. 97008, University of Illinois at Urbana-Champaign (1997)Google Scholar
  17. 17.
    Bazargani, M., Mateus, L., Loja, M.A.R.: Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud. In: Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014). IEEE (2014)Google Scholar
  18. 18.
    Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995)MATHMathSciNetGoogle Scholar
  19. 19.
    Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10, 371–395 (2002)CrossRefGoogle Scholar
  20. 20.
    Deb, K., Agrawal, S.: A niched-penalty approach for constraint handling in genetic algorithms. In: Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, pp. 235–243. Springer (1999)Google Scholar
  21. 21.
    Deb, K., Deb, D.: Analysing mutation schemes for real-parameter genetic algorithms. Int. J. Artif. Intell. Soft Comput. (IJAISC) 4, 1–28 (2014)CrossRefGoogle Scholar
  22. 22.
    Harik, G.R., Cantú-Paz, E., Goldberg, D.E., Miller, B.L.: The gambler’s ruin problem, genetic algorithms, and the sizing of populations. Evol. Comput. 7, 231–253 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mosab Bazargani
    • 1
  • Luís Mateus
    • 2
  • Maria Amélia R. Loja
    • 1
    • 3
  1. 1.LAETA, IDMEC, Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal
  2. 2.CIAUD, Faculdade de ArquiteturaUniversidade de LisboaLisbonPortugal
  3. 3.ISEL, IPL, Instituto Superior de Engenharia de LisboaLisbonPortugal

Personalised recommendations