3D Research

, 6:44 | Cite as

Extraction of Reliable Primitives from Unorganized Point Clouds

3DR Express
First Online:
Received:
Revised:
Accepted:

Abstract

Manufactured objects are the combination of some basic geometric primitives such as planes, spheres, cylinders, etc. Existing methods often approximate data points by geometric primitives for surface reconstruction. In this context, the extraction of reliable geometric primitives from unorganized point clouds is addressed in this paper. First, curved surfaces (cylinders and spheres) are extracted robustly from point clouds. Then the points associated with these primitives are removed. Finally, planar surfaces are extracted from the remaining points using a RANSAC-based approach. The performance of the proposed framework is tested on synthetic and scanned data. The performance analysis shows that the proposed method outperforms existing methods and may be used for various applications.

Keywords

Plane Cylinder Sphere Primitive extraction Point clouds 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This research project was supported by the NSERC/Creaform Industrial Research Chair on 3-D Scanning. The authors thank the AIM@SHAPE Shape Repository for making the models available. We are grateful to Annette Schwerdtfeger for proofreading the manuscript and to Van-Tung Nguyen for providing the Sphere Box model.

References

  1. 1.
    Musialski, P., Wonka, P., Aliaga, D. G., Wimmer, M., Gool, L., & Purgathofer, W. (2013). A survey of Urban reconstruction. In F. S. Nooruddin (Ed.), Computer graphics forum (Vol. 32, pp. 146–177). Hoboken: Wiley Online Library.Google Scholar
  2. 2.
    Berger, M., Tagliasacchi, A., Seversky, L. M., Alliez, P., Levine, J.-A., Sharf, A., et al. (2014). State of the art in surface reconstruction from point clouds. In S. Lefebvre & M. Spagnuolo (Eds.), Eurographics 2014—state of the art reports. Aire-la-Ville: The Eurographics Association.Google Scholar
  3. 3.
    Schnabel, R., Wahl, R., & Klein, R. (2007). Efficient RANSAC for point-cloud shape detection. Computer Graphics Forum, 26(2), 214–226.CrossRefGoogle Scholar
  4. 4.
    Li, Y., Wu, X., Chrysathou, Y., Sharf, A., Cohen-Or, D., & Mitra, N. J. (2011). Globfit: Consistently fitting primitives by discovering global relations. ACM Transactions on Graphics, 30, 52:1–52:12.Google Scholar
  5. 5.
    Borrmann, D., Elseberg, J., Lingemann, K., & Nüchter, A. (2011). “The 3D Hough transform for plane detection in point clouds: A review and a new accumulator design,” 3D. Research, 2(2), 1–13.Google Scholar
  6. 6.
    Rabbani, T., & van den Heuvel, F. (2005). Efficient Hough transform for automatic detection of cylinders in point clouds. In ISPRS WG III/3, III/4 (vol. 3, pp. 60–65).Google Scholar
  7. 7.
    Cohen-Steiner, D., Alliez, P., & Desbrun, M. (2004). Variational shape approximation. ACM Transactions on Graphics, 23(3), 905–914.CrossRefGoogle Scholar
  8. 8.
    Vieira, M., & Shimada, K. (2005). Surface mesh segmentation and smooth surface extraction through region growing. Computer Aided Geometric Design, 22(8), 771–792.CrossRefMathSciNetMATHGoogle Scholar
  9. 9.
    Lafarge, F., & Mallet, C. (2012). Creating large-scale city models from 3D-point clouds: A robust approach with hybrid representation. International Journal of Computer Vision, 99(1), 69–85.CrossRefMathSciNetGoogle Scholar
  10. 10.
    Rabbani, T., Dijkman, S., van den Heuvel, F., & Vosselman, G. (2007). An integrated approach for modelling and global registration of point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 61(6), 355–370.CrossRefGoogle Scholar
  11. 11.
    Bolles, R. C., & Fischler, M. A. (1981). A RANSAC-based approach to model fitting and its application to finding cylinders in range data. In Proceedings of the Seventh International Joint Conference on Artificial Intelligence—volume 2, IJCAI’81, (pp. 637–643). San Francisco, CA: Morgan Kaufmann Publishers Inc.Google Scholar
  12. 12.
    Lavva, I., Hameiri, E., & Shimshoni, I. (2008). Robust methods for geometric primitive recovery and estimation from range images. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 38(3), 826–845.CrossRefGoogle Scholar
  13. 13.
    Liu, J., & Wu, Z. (2014). An adaptive approach for primitive shape extraction from point clouds. Optik: International Journal for Light and Electron Optics, 125, 2000–2008.CrossRefGoogle Scholar
  14. 14.
    Duda, R. O., & Hart, P. E. (1972). Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, 15, 11–15.CrossRefMATHGoogle Scholar
  15. 15.
    Holz, D., & Behnke, S. (2013). Fast range image segmentation and smoothing using approximate surface reconstruction and region growing. Intelligent Autonomous Systems, 12, 61–73.CrossRefGoogle Scholar
  16. 16.
    Camurri, M., Vezzani, R., & Cucchiara, R. (2014). 3D Hough transform for sphere recognition on point clouds. Machine Vision and Applications, 25(7), 1877–1891.CrossRefGoogle Scholar
  17. 17.
    Tran, T.-T., Cao, V.-T., & Laurendeau, D. (2015). Extraction of cylinders and estimation of their parameters from point clouds. Computers & Graphics, 46, 345–357.CrossRefGoogle Scholar
  18. 18.
    Tran, T.-T., Cao, V.-T., & Laurendeau, D. (2015). esphere: Extracting spheres from unorganized point clouds. The Visual Computer, 2015, 1–18.Google Scholar
  19. 19.
    Tran, T., Cao, V., Nguyen, V., Ali, S., & Laurendeau, D. (2014) Automatic method for sharp feature extraction from 3D data of man-made objects. In GRAPP 2014—Proceedings of the Ninth International Conference on Computer Graphics Theory and Applications, (pp. 112–119), Lisbon, 5–8 January, 2014.Google Scholar
  20. 20.
    Lai, H.-C., Chang, Y.-H., & Lai, J.-Y. (2009). Development of feature segmentation algorithms for quadratic surfaces. Advances in Engineering Software, 40(10), 1011–1022.CrossRefMATHGoogle Scholar
  21. 21.
    Wang, J., Gu, D., Yu, Z., Tan, C., & Zhou, L. (2012). A framework for 3D model reconstruction in reverse engineering. Computers & Industrial Engineering, 63(4), 1189–1200.CrossRefGoogle Scholar
  22. 22.
    Attene, M., Falcidieno, B., & Spagnuolo, M. (2006). Hierarchical mesh segmentation based on fitting primitives. The Visual Computer, 22(3), 181–193.CrossRefGoogle Scholar
  23. 23.
    Attene, M., & Patanè, G. (2010). Hierarchical structure recovery of point-sampled surfaces. Computer Graphics Forum, 29(6), 1905–1920.CrossRefGoogle Scholar
  24. 24.
    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., & Stuetzle, W. (1992). Surface reconstruction from unorganized points. In Proceedings of the Nineteenth annual conference on Computer graphics and interactive techniques, (pp. 71–78), New York.Google Scholar
  25. 25.
    Garland, M., & Heckbert, P.-S. (1998). Simplifying surfaces with color and texture using quadric error metrics. In Proceedings of the Conference on Visualization '98, (pp. 263–269). Los Alamitos, CA: IEEE Computer Society Press.Google Scholar
  26. 26.
    Pratt, V. (1987). Direct least-squares fitting of algebraic surfaces. In SIGGRAPH ’87, Proceedings of the Fourteenth Annual Conference on Computer Graphics and Interactive Techniques, (pp. 145–152). New York, NY: ACM.Google Scholar
  27. 27.
    Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619.CrossRefGoogle Scholar
  28. 28.
    Tran, T.-T., Ali, S., & Laurendeau, D. (2013). Automatic sharp feature extraction from point clouds with optimal neighbor size. In MVA ’13, Proceedings of the Thirteenth IAPR International Conference on Machine Vision Applications, Kyoto.Google Scholar

Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Trung-Thien Tran
    • 1
  • Van-Toan Cao
    • 1
  • Denis Laurendeau
    • 1
  1. 1.CVSL LaboratoryLaval UniversityQuebecCanada

Personalised recommendations