The Visual Computer

, Volume 34, Issue 5, pp 659–673 | Cite as

A self-adaptive segmentation method for a point cloud

  • Yuling Fan
  • Meili Wang
  • Nan Geng
  • Dongjian He
  • Jian Chang
  • Jian J. Zhang
Original Article


The segmentation of a point cloud is one of the key technologies for three-dimensional reconstruction, and the segmentation from three-dimensional views can facilitate reverse engineering. In this paper, we propose a self-adaptive segmentation algorithm, which can address challenges related to the region-growing algorithm, such as inconsistent or excessive segmentation. Our algorithm consists of two main steps: automatic selection of seed points according to extracted features and segmentation of the points using an improved region-growing algorithm. The benefits of our approach are the ability to select seed points without user intervention and the reduction of the influence of noise. We demonstrate the robustness and effectiveness of our algorithm on different point cloud models and the results show that the segmentation accuracy rate achieves 96%.


Point cloud Segmentation Seed point Region growing 



This work was partially funded by the National High-tech research and Development Program (863 Program: 2013AA10230402), National Natural Science Foundation of China (61402374), and the China Postdoctoral Science Foundation (2014M562457). The authors acknowledge the authors of [26], Shenzhen Key Lab of Visual Computing and Visual Analytics for the source data and the models.


  1. 1.
    Aiteanu, F., Klein, R.: Hybrid tree reconstruction from inhomogeneous point clouds. Vis. Comput. 30(6–8), 763–771 (2014)CrossRefGoogle Scholar
  2. 2.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 335–336. ACM (1998)Google Scholar
  3. 3.
    Castillo, E., Liang, J., Zhao, H.: Point cloud segmentation and denoising via constrained nonlinear least squares normal estimates. Innovations for shape analysis. Springer, Berlin (2013)zbMATHGoogle Scholar
  4. 4.
    Chen, X., Golovinskiy, A., Funkhouser, T.: A benchmark for 3D mesh segmentation. ACM Trans. Graph. (TOG) 28(3), 73 (2009)CrossRefGoogle Scholar
  5. 5.
    Clarenz, U., Griebel, M., et al.: Feature sensitive multiscale editing on surfaces. Vis. Comput. 20(5), 329–343 (2004)CrossRefGoogle Scholar
  6. 6.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  7. 7.
    Dai, M., Zhang, X., et al.: Segmentation of point cloud scanned from trees. In: Workshop on Community Based 3D Content and Its Applications in Mobile Internet Environments, ACCV (2009)Google Scholar
  8. 8.
    Demir, I., Aliaga, D.G., Benes, B.: Coupled segmentation and similarity detection for architectural models. ACM Trans. Graph. (TOG) 34(4), 104 (2015)CrossRefGoogle Scholar
  9. 9.
    Dorninger, P., Nothegger, C.: 3D segmentation of unstructured point clouds for building modelling. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 35/W49A(3), 191–196 (2007)Google Scholar
  10. 10.
    Fayolle, P., Pasko, A.: Segmentation of discrete point clouds using an extensible set of templates. Vis. Comput. 29(5), 449–465 (2013)CrossRefGoogle Scholar
  11. 11.
    Gelfand, N., Guibas, L.: Shape segmentation using local slippage analysis. In: Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing, pp. 214–223. ACM (2004)Google Scholar
  12. 12.
    Gomes, R.B., da Silva, B.M.F.: Efficient 3D object recognition using foveated point clouds. Comput. Graph. 37(5), 496–508 (2013)CrossRefGoogle Scholar
  13. 13.
    Guillaume, L., Florent, D., Atilla, B.: Curvature tensor based triangle mesh segmentation with boundary rectification. In: Computer Graphics International, 2004. Proceedings, pp. 10–25. IEEE (2004)Google Scholar
  14. 14.
    Huang, H., Wu, S., et al.: L1-medial skeleton of point cloud. ACM Trans. Graph. 32(4), 65–1 (2013)Google Scholar
  15. 15.
    Kaick, O.V., Fish, N., Kleiman, Y., Asafi, S., Cohen-Or, D.: Shape segmentation by approximate convexity analysis. ACM Trans. Graph. (TOG) 34(1), 4 (2014)CrossRefGoogle Scholar
  16. 16.
    Lari, Z., Habib, A.: A novel hybrid approach for the extraction of linear/cylindrical features from laser scanning data. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2, 151–156 (2013)CrossRefGoogle Scholar
  17. 17.
    Li, Y., Wu, X., et al.: Globfit: consistently fitting primitives by discovering global relations. ACM Trans. Graph. (TOG) 30(4), 52 (2011)MathSciNetGoogle Scholar
  18. 18.
    Marshall, D., Lukacs, G., Martin, R.: Robust segmentation of primitives from range data in the presence of geometric degeneracy. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 304–314 (2001)CrossRefGoogle Scholar
  19. 19.
    Ochmann, S., Vock, R., Wessel, R., Klein, R.: Automatic reconstruction of parametric building models from indoor point clouds. Comput. Graph. 54, 94–103 (2016)CrossRefGoogle Scholar
  20. 20.
    Pu, S., Vosselman, G., et al.: Automatic extraction of building features from terrestrial laser scanning. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36(5), 25–27 (2006)Google Scholar
  21. 21.
    Rabbani, T., Van Den Heuvel, F., Vosselmann, G.: Segmentation of point clouds using smoothness constraint. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36(5), 248–253 (2006)Google Scholar
  22. 22.
    Richtsfeld, M., Vincze, M.: Point cloud segmentation based on radial reflection. Computer analysis of images and patterns, pp. 955–962. Springer, Berlin (2009)CrossRefGoogle Scholar
  23. 23.
    Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)CrossRefGoogle Scholar
  24. 24.
    Wang, J., Shan, J.: Segmentation of lidar point clouds for building extraction. In: American Society for Photogrammetry and Remote Sensing Annual Conference, Baltimore, MD, pp. 9–13 (2009)Google Scholar
  25. 25.
    Yamauchi, H., Lee, S., et al.: Feature sensitive mesh segmentation with mean shift. In: Shape Modeling and Applications, 2005 International Conference, pp. 236–243. IEEE (2005)Google Scholar
  26. 26.
    Yücer, K., Sorkine-Hornung, A., et al.: Efficient 3D object segmentation from densely sampled light fields with applications to 3D reconstruction. ACM Trans. Graph. (TOG) 35(3), 22 (2016)CrossRefGoogle Scholar
  27. 27.
    Zhana, Q., Liangb, Y., Xiaoa, Y.: Color-based segmentation of point clouds. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 38, 248–252 (2009)Google Scholar
  28. 28.
    Zhang, Y., Geng, G., et al.: A statistical approach for extraction of feature lines from point clouds. Comput. Graph. 56, 31–45 (2016)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Yuling Fan
    • 1
  • Meili Wang
    • 1
  • Nan Geng
    • 1
  • Dongjian He
    • 2
  • Jian Chang
    • 3
  • Jian J. Zhang
    • 3
  1. 1.College of Information EngineeringNorthwest A&F UniversityXianyangChina
  2. 2.College of Mechanical and Electronic EngineeringNorthwest A&F UniversityXianyangChina
  3. 3.National Centre for Computer Animation, Media SchoolBournemouth UniversityBournemouthUK

Personalised recommendations