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Intelligent Reconstruction and Assembling of Pipeline from Point Cloud Data in Smart Plant 3D

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9315))

Abstract

The laser-scanned data of subsisting industrial pipeline plants are not only astronomically immense, but are withal intricately entwined like a net. The users must identify 3D points corresponding to each pipeline to be modelled in immensely colossal laser-scanned data sets. To accurately identify the 3D points corresponding to each pipeline, the users need to have some cognizance of direction and design of the pipelines. In addition, manually identifying each pipeline from gigantic and intricate scanned data is proximately infeasible, time-consuming and cumbersome process. In order to simplify and make the process more facile for reconstruction process an intelligent way of reconstruction and assembling of pipeline from point cloud data in Smart Plant 3D (SP3D) is proposed. The presented results shows that the proposed method indeed contribute automation of 3D pipeline model.

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References

  1. Wehr, A., Lohr, U.: Airborne laser scanning—an introduction and overview. ISPRS J. Photogramm. Remote Sens. 54, 68–82 (1999)

    Article  Google Scholar 

  2. Leica Cyclone., Heerberg, Switzerland, Leica Geosystems AG

    Google Scholar 

  3. Smart Plant 3D., Huntsville, AL, Intergraph Corporation

    Google Scholar 

  4. Son, H., Kim, C., Kim, C.: Fully automated as-built 3D pipeline segmentation based on curvature computation from laser-scanned data. In: Proceedings of Computing in Civil Engineering–Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering, pp. 765–772 (2013)

    Google Scholar 

  5. Bosché, F.: “Model Spell Checker” for Primitive-based As-built Modeling in Construction. University of Texas at Austin, Austin (2003)

    Google Scholar 

  6. Rabbani, T., van den Heuvel, F., Vosselmann, G.: Segmentation of point clouds using smoothness constraint. Inter. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36, 248–253 (2006)

    Google Scholar 

  7. Rusu, R.B., Cousins, S.: 3D is here: point cloud library (pcl). In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–4. IEEE (2011)

    Google Scholar 

  8. Golovinskiy, A., Kim, V.G., Funkhouser, T.: Shape-based recognition of 3D point clouds in urban environments. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2154–2161. IEEE (2009)

    Google Scholar 

  9. Huang, J., You, S.: Detecting objects in scene point cloud: a combinational approach. In: 2013 International Conference on 3D Vision-3DV 2013, pp. 175–182. IEEE (2013)

    Google Scholar 

  10. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 27 (2011)

    Google Scholar 

  11. Rabbani, T., Van Den Heuvel, F.: Efficient hough transform for automatic detection of cylinders in point clouds. In: ISPRS WG III/3, III/4 3, pp. 60–65 (2005)

    Google Scholar 

  12. Do Carmo, M.P.: Differential Geometry of Curves and Surfaces. Prentice-Hall, Upper Saddle River (1976)

    MATH  Google Scholar 

  13. Golub, G.H., Van Loan, C.F.: Matrix computations. JHU Press, Baltimore (2012)

    MATH  Google Scholar 

  14. Kimme, C., Ballard, D., Sklansky, J.: Finding circles by an array of accumulators. Commun. ACM 18, 120–122 (1975)

    Article  MATH  Google Scholar 

  15. Microsoft SQL Server: Microsoft Corporation, Redmond (2008)

    Google Scholar 

Download references

Acknowledgements

This work was supported by Commercializations Promotion Agency for R & D Outcomes – Grant funded by the Ministry of Science, ICT and future Planning - 2013A000019.

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Correspondence to Young Ho Chai .

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© 2015 Springer International Publishing Switzerland

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Holi, P., Park, S.S., Patil, A.K., Kumar, G.A., Chai, Y.H. (2015). Intelligent Reconstruction and Assembling of Pipeline from Point Cloud Data in Smart Plant 3D. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_36

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  • DOI: https://doi.org/10.1007/978-3-319-24078-7_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24077-0

  • Online ISBN: 978-3-319-24078-7

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