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Direct Point Cloud Visualization Using T-spline with Edge Detection

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Recent Advances in Soft Computing (ICSC-MENDEL 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 576))

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Abstract

This article presents a hybrid method for a processing of a cloud point. Proposed method is suitable for reverse engineering where the need of precise model representation is essential. Our method is composed of mathematical representation using T-spline surfaces and edge extraction using k-neighborhood and Gauss mapping. The advantages of this method that we are able to find mathematical expression of the model where modification of parameters expresses the edges directly.

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References

  1. Alharthy, A., Bethel, J.: Heuristic filtering and 3D feature extraction from LIDAR data. In: ISPRS Commission III, Symposium 2002, pp. 23–28 (2002)

    Google Scholar 

  2. Attene, M., Falcidieno, B., Rossignac, J., Spagnuolo, M.: Sharpen bend: recovering curved sharp edges in triangle meshes produced by feature-insensitive sampling. IEEE Trans. Visual Comput. Graph. 11(2), 181–192 (2005)

    Article  Google Scholar 

  3. Baining, G.: Surface reconstruction: from points to splines. Comput. Aided Des. 29(4), 269–277 (1997)

    Article  Google Scholar 

  4. Curless, B.: From range scans to 3D models. SIGGRAPH Comput. Graph. 33(4), 38–41 (1999)

    Article  Google Scholar 

  5. Demarsin, K., Vandestraeten, D., Volodine, T., Roose, D.: Detection of closed sharp edges in point cloud using normal estimation and graph theory. Comput. Aided Des. 39, 276–283 (2007)

    Article  Google Scholar 

  6. Farin, G.: Curves and Surfaces for Computer Aided Geometric Design: A Practical Guide, 4th edn. Academic Press, New York (1997)

    MATH  Google Scholar 

  7. Finnigan, G.T.: Arbitrary Degree T-Splines. All theses and Dissertations. Paper 1431 (2008)

    Google Scholar 

  8. Gumhold, S., Wang, X., Macleod, R.: Feature extraction from point clouds. In: Gumhold, S. (ed.) Proceedings of the 10th International Meshing Roundtable, pp. 293–305. Sandia National Laboratory (2001)

    Google Scholar 

  9. Hildebrand, K., Polthier, K., Wardetzky, M.: Smooth feature lines on surface meshes. In: Proceedings of the Third Eurographics Symposium on Geometry Processing, SGP 2005, Article 85. Aire-la-Ville, Switzerland (2005)

    Google Scholar 

  10. Hubeli, A., Gross, M.: Multiresolution feature extraction for unstructured meshes. In: Proceedings of IEEE Visualization, pp. 287–294 (2001)

    Google Scholar 

  11. Klecka, J., Horak, K.: Fusion of 3D model and uncalibrated stereo reconstruction. In: Matoušek, R. (ed.) Mendel 2015. AISC, vol. 378, pp. 343–351. Springer, Cham (2015). doi:10.1007/978-3-319-19824-8_28

    Chapter  Google Scholar 

  12. Lafarge, F., Mallet, C.: Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation. Int. J. Comput. Vis. 99(1), 69–85 (2012)

    Article  MathSciNet  Google Scholar 

  13. Lee, D.T., Schachter, B.J.: Two algorithms for constructing a Delaunay triangulation. Int. J. Comput. Inform. Sci. 9(3), 219–242 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  14. Liu, L., Zhang, Y.J., Wei, X.: Weighted T-splines with application in reparameterizing trimmed NURBS surfaces. Comput. Methods Appl. Mech. Eng. 295, 108–126 (2015)

    Article  MathSciNet  Google Scholar 

  15. Martisek, D., Prochazkova, J.: Relation between algebraic and geometric view on NURBS tensor surfaces. Appl. Math. 5, 419–430 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. Ohtake, Y., Belyaev, A.: Automatic detection of geodesic ridges and ravines on polygonal surfaces. J. Three Dimensional Images 15(1), 127–132 (2001)

    Google Scholar 

  17. Pang, G., Qiu, R., Huang, J., You, S., Neumann, U.: Automatic 3D industrial point cloud modeling and recognition. In: Machine Vision Applications (MVA), pp. 22–25 (2015)

    Google Scholar 

  18. Patraucean, V., Armeni, I., Nahangi, M., Yeung, J., Brilakis, I., Haas, C.: State of research in automatic as-built modelling. Adv. Eng. Inform. 29(2), 162–171 (2015)

    Article  Google Scholar 

  19. Peter, S., Drysdale, R.L.S.: A comparison of sequential Delaunay triangulation algorithms. In: Peter, S. (ed.) Proceedings of the 11th Annual Symposium on Computational Geometry, SCG 1995, pp. 61–70. ACM, New York (1995)

    Google Scholar 

  20. Piegl, L., Tiller, W.: The NURBS Book. Springer, Berlin (2002)

    MATH  Google Scholar 

  21. Sederberg, T.W., Zheng, J., Bakenov, A., Nasri, A.: T-splines and T-NURCCS. ACM Trans. Graph. 22(3), 477–483 (2003)

    Article  Google Scholar 

  22. Sederberg, T.W., Zheng, J., Cardon, D.L., Lyche, T.: T-splines simplification and local refinement. ACM Trans. Graph. 23(3), 276–283 (2004)

    Article  Google Scholar 

  23. Shewchuk, J.R.: Triangle: engineering a 2D quality mesh generator and Delaunay triangulator. In: Lin, M.C., Manocha, D. (eds.) WACG 1996. LNCS, vol. 1148, pp. 203–222. Springer, Heidelberg (1996). doi:10.1007/BFb0014497

    Chapter  Google Scholar 

  24. Somani, N., Perzylo, A., Cai, C., Rickert, M., Knoll, A.: Object detection using boundary representations of primitive shapes. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 108–113 (2015)

    Google Scholar 

  25. Starha, P., Martisek, D., Matousek, R.: Numerical methods of object reconstruction using the method of moments. In: Proceedings of 20th International Conference on Soft Computing - Mendel 2014. Mendel Series, vol. 2014, Brno, pp. 241–248 (2014). ISSN: 1803–3814

    Google Scholar 

  26. Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: Point feature extraction on 3D range scans taking into account object boundaries. In: Robotics and Automation (ICRA), pp. 2601–2608 (2011)

    Google Scholar 

  27. Stylianou, G., Farin, G.: Crest lines extraction from 3D triangulated meshes. In: Hierarchical and Geometrical Methods in Scientific Visualization, pp. 269–281 (2003)

    Google Scholar 

  28. Verma, V., Kumar, R., Hsu, S.: 3D building detection and modeling from aerial LIDAR data. IEEE Comput. Vis. Pattern Recogn. 2, 2213–2220 (2006)

    Google Scholar 

  29. Vosselman, V.: Building reconstruction using planar faces in very hight density data. In: International Archives of Photogrammetry and Remote Sensing, pp. 87–92 (1999)

    Google Scholar 

  30. Wang, Y., Ewert, D., Schilberg, D., Jeschke, S.: Edge extraction by merging 3D point cloud and 2D image data. In: Emerging Technologies for a Smarter World (CEWIT), pp. 1–6 (2013)

    Google Scholar 

  31. Weber, C., Hahmann, S., Hagen, H.: 2010. Sharp feature detection in point clouds. In: Shape Modeling International Conference (SMI 2010), pp. 175–186 (2010)

    Google Scholar 

  32. Weinkauf, T., Gnther, D.: Separatrix persistence: extraction of salient edges on surfaces using topological methods. Comput. Graph. Forum 28(5), 1519–1528 (2009)

    Article  Google Scholar 

  33. You, S., Hu, J., Neumann, U., Fox, P.: Urban site modeling from LiDAR. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003. LNCS, vol. 2669, pp. 579–588. Springer, Heidelberg (2003). doi:10.1007/3-540-44842-X_59

    Chapter  Google Scholar 

  34. Zhang, G., Vela, P.A., Brilakis, I.: Detecting, fitting, and classifying surface primitives for infrastructure point cloud data. In: Computing in Civil Engineering, pp. 589–596 (2013)

    Google Scholar 

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Acknowledgments

This work was supported by Project LO1202 by financial means from the Ministry of Education, Youth and Sports under the National Sustainability Programme I.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Mechanical Engineering, Brno University of Technology, Technicka 2, Brno, Czech Republic

    Jana Prochazkova & Jiri Kratochvil

Authors
  1. Jana Prochazkova
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  2. Jiri Kratochvil
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Correspondence to Jana Prochazkova .

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Editors and Affiliations

  1. Department of Applied Computer Science, Institute of Automation and Computer Science, Faculty of Mechanical Engineering, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Prochazkova, J., Kratochvil, J. (2017). Direct Point Cloud Visualization Using T-spline with Edge Detection. In: Matoušek, R. (eds) Recent Advances in Soft Computing. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 576. Springer, Cham. https://doi.org/10.1007/978-3-319-58088-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-58088-3_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58087-6

  • Online ISBN: 978-3-319-58088-3

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