Ensembles for Normal and Surface Reconstructions

  • Mincheol Yoon
  • Yunjin Lee
  • Seungyong Lee
  • Ioannis Ivrissimtzis
  • Hans-Peter Seidel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4077)


The majority of the existing techniques for surface reconstruction and the closely related problem of normal estimation are deterministic. Their main advantages are the speed and, given a reasonably good initial input, the high quality of the reconstructed surfaces. Nevertheless, their deterministic nature may hinder them from effectively handling incomplete data with noise and outliers. In our previous work [1], we applied a statistical technique, called ensembles, to the problem of surface reconstruction. We showed that an ensemble can improve the performance of a deterministic algorithm by putting it into a statistics based probabilistic setting. In this paper, with several experiments, we further study the suitability of ensembles in surface reconstruction, and also apply ensembles to normal estimation. We experimented with a widely used normal estimation technique [2] and Multi-level Partitions of Unity implicits for surface reconstruction [3], showing that normal and surface ensembles can successfully be combined to handle noisy point sets.


Point Cloud Normal Estimation Normal Ensemble Ensemble Member Surface Reconstruction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lee, Y., Yoon, M., Lee, S., Ivrissimtzis, I., Seidel, H.P.: Ensembles for surface reconstruction. In: Proc. Pacific Graphics 2005, pp. 115–117 (2005)Google Scholar
  2. 2.
    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. In: Computer Graphics (Proc. ACM SIGGRAPH 1992), pp. 71–78 (1992)Google Scholar
  3. 3.
    Ohtake, Y., Belyaev, A., Alexa, M., Turk, G., Seidel, H.P.: Multi-level partition of unity implicits. ACM Transactions on Graphics 22(3), 463–470 (2003)CrossRefGoogle Scholar
  4. 4.
    Bernardini, F., Rushmeier, H.: The 3D model acquisition pipeline. Computer Graphics Forum 21(2), 149–172 (2002)CrossRefGoogle Scholar
  5. 5.
    Weyrich, T., Pauly, M., Keiser, R., Heinzle, S., Scandella, S., Gross, M.: Post-processing of scanned 3D surface data. In: Proc. Eurographics Symposium on Point-Based Graphics 2004, pp. 85–94 (2004)Google Scholar
  6. 6.
    Sainz, M., Pajarola, R., Mercade, A., Susin, A.: A simple approach for point-based object capturing and rendering. IEEE Computer Graphics and Applications 24(4), 24–33 (2004)CrossRefGoogle Scholar
  7. 7.
    Schapire, R.E.: The strength of weak learnability. Machine Learning 5(2), 197–227 (1990)Google Scholar
  8. 8.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine Learning: Proc. the 13th International Conference, pp. 148–156 (1996)Google Scholar
  9. 9.
    Ivrissimtzis, I., Lee, Y., Lee, S., Jeong, W.K., Seidel, H.P.: Neural mesh ensembles. In: Proc. 3D Data Processing, Visualization, and Transmission, 2nd International Symposium on (3DPVT 2004), pp. 308–315 (2004)Google Scholar
  10. 10.
    Ivrissimtzis, I., Jeong, W.K., Lee, S., Lee, Y., Seidel, H.P.: Neural meshes: Surface reconstruction with a learning algorithm. Technical Report MPI-I-2004-4-005, Max-Planck-Institut für Informatik, Saarbrücken (2004)Google Scholar
  11. 11.
    Pauly, M., Keiser, R., Kobbelt, L.P., Gross, M.: Shape modeling with point-sampled geometry. ACM Transactions on Graphics 22(3), 641–650 (2003)CrossRefGoogle Scholar
  12. 12.
    Gopi, M., Krishnan, S., Silva, C.: Surface reconstruction based on lower dimensional localized Delaunay triangulation. Computer Graphics Forum (Proc. Eurographics 2000) 19(3), 467–478 (2000)CrossRefGoogle Scholar
  13. 13.
    Hu, G., Xu, J., Miao, L., Peng, Q.: Bilateral estimation of vertex normal for point-sampled models. In: Proc. Computational Science and Its Applications (ICCSA 2005), pp. 758–768 (2005)Google Scholar
  14. 14.
    Mitra, N.J., Nguyen, A., Guibas, L.: Estimating surface normals in noisy point cloud data. Special Issue of International Journal of Computational Geometry and Applications 14(4-5), 261–276 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Dey, T.K., Li, G., Sun, J.: Normal estimation for point clouds: A comparison study for a voronoi based method. In: Proc. Eurographics Symposium on Point-Based Graphics 2005, pp. 39–46 (2005)Google Scholar
  16. 16.
    Jones, T.R., Durand, F., Zwicker, M.: Normal improvement for point rendering. IEEE Computer Graphics and Applications 24(4), 53–56 (2004)CrossRefGoogle Scholar
  17. 17.
    Bajaj, C., Bernardini, F., Xu, G.: Automatic reconstruction of surfaces and scalar fields from 3D scans. In: Proc. ACM SIGGRAPH 1995, pp. 109–118 (1995)Google Scholar
  18. 18.
    Krishnamurthy, V., Levoy, M.: Fitting smooth surfaces to dense polygon meshes. In: Proc. ACM SIGGRAPH 1996, pp. 313–324 (1996)Google Scholar
  19. 19.
    Amenta, N., Bern, M., Kamvysselis, M.: A new Voronoi-based surface reconstruction algorithm. In: Proc. ACM SIGGRAPH 1998, pp. 415–421 (1998)Google Scholar
  20. 20.
    Amenta, N., Choi, S., Kolluri, R.K.: The power crust, unions of balls, and the medial axis transform. Computational Geometry: Theory and Applications 19(2-3), 127–153 (2001)zbMATHMathSciNetGoogle Scholar
  21. 21.
    Dey, T.K., Goswami, S.: Tight cocone: A water-tight surface reconstructor. Journal of Computing and Information Science in Engineering 3(4), 302–307 (2003)CrossRefGoogle Scholar
  22. 22.
    Kolluri, R., Shewchuk, J.R., O’Brien, J.F.: Spectral surface reconstruction from noisy point clouds. In: Proc. Symposium on Geometry Processing 2004 (SGP 2004), pp. 11–21 (2004)Google Scholar
  23. 23.
    Carr, J., Beatson, R., Cherrie, J., Mitchell, T., Fright, W., McCallum, B., Evans, T.: Reconstruction and representation of 3D objects with radial basis functions. In: Proc. ACM SIGGRAPH 2001, pp. 67–76 (2001)Google Scholar
  24. 24.
    Buss, S.R., Fillmore, J.P.: Spherical averages and applications to spherical splines and interpolation. ACM Transactions on Graphics 20(2), 95–126 (2001)CrossRefGoogle Scholar
  25. 25.
    Cignoni, P., Rocchini, C., Scopigno, R.: Metro: Measuring error on simplified surfaces. Computer Graphics Forum 17(2), 167–174 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mincheol Yoon
    • 1
  • Yunjin Lee
    • 1
  • Seungyong Lee
    • 1
  • Ioannis Ivrissimtzis
    • 2
  • Hans-Peter Seidel
    • 3
  1. 1.POSTECH 
  2. 2.Coventry University 
  3. 3.MPI Informatik 

Personalised recommendations