Advertisement

Surface Registration Markers from Range Scan Data

  • John Rugis
  • Reinhard Klette
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4040)

Abstract

We introduce a data processing pipeline designed to generate registration markers from range scan data. This approach uses curvature maps and histogram-templates to identify local surface features. The noise associated with real-world scans is addressed using a (common) Gauss filter and expansion-segmentation. Experimental results are presented for data from The Digital Michelangelo Project.

Keywords

Positive Curvature Iterative Close Point Curvature Estimator Iterative Close Point Guard Ring 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Balsys, R.J., Suffern, K.G.: Visualisation of implicit surfaces. Computers & Graphics 25, 89–107 (2001)CrossRefGoogle Scholar
  2. 2.
    Brown, B.J., Rusinkiewicz, S.: Non-rigid range-scan alignment using thin-plate splines. In: Proc. 3D Data Processing Visualization Transm., pp. 759–765 (2004)Google Scholar
  3. 3.
    Chang, M.C., Leymarie, F.F., Kimia, B.B.: 3D shape registration using regularized medial scaffolds. In: Proc. 3D Data Processing Visualization Transmission, pp. 987–994 (2004)Google Scholar
  4. 4.
    Davies, A., Samuels, P.: An Introduction to Computational Geometry for Curves and Surfaces. Oxford University Press, Oxford (1996)MATHGoogle Scholar
  5. 5.
    Gatzke, T., Grimm, C., Garland, M., Zelinka, S.: Curvature maps for local shape comparison. In: Proc. Shape Modeling and Applications, pp. 244–253 (2005)Google Scholar
  6. 6.
    Gauss, C.F.: General Investigations of Curved Surfaces (Reprint of publications from 1825 and 1827). Dover Publications, New York (2005)Google Scholar
  7. 7.
    Hermann, S., Klette, R.: Multigrid analysis of curvature estimators. In: Proc. Image and Vision Computing, New Zealand, pp. 108–112 (2003)Google Scholar
  8. 8.
    Klette, R., Stiehl, H.S., Viergever, M.A., Vincken, K.L. (eds.): Performance Characterization in Computer Vision. Kluwer, Dordrecht (2000)MATHGoogle Scholar
  9. 9.
    Klette, R., Rosenfeld, A.: Digital Geometry. M. Kaufmann, San Francisco (2004)MATHGoogle Scholar
  10. 10.
    Klette, R., Schlüns, K., Koschan, A.: Computer Vision - Three-Dimensional Data from Images. Springer, Singapore (1998)MATHGoogle Scholar
  11. 11.
    Levoy, M.: The digital Michelangelo project. In: Proc. 3D Digital Imaging Modeling, pp. 34–43 (1999)Google Scholar
  12. 12.
    Levoy, M., Pulli, K., Curless, B., Rusinkiewicz, S., Koller, D., Pereira, L., Ginzton, M., Anderson, S., Davis, J., Ginsberg, J., Shade, J., Fulk, D.: The digital Michelangelo project: 3D scanning of large statues. In: Proc. SIGGRAPH, pp. 131–144 (2000)Google Scholar
  13. 13.
    Parent, P., Zucker, S.: Trace inference, curvature consistency, and curve detection. IEEE Trans. Pattern Analysis and Machine Intelligence 11, 823–839 (1989)CrossRefGoogle Scholar
  14. 14.
    Pulli, K.: Multiview registration for large data sets. In: Proc. Int. Conf. 3D Digital Imaging and Modeling, pp. 160–168 (1999)Google Scholar
  15. 15.
    Pulli, K., Curless, B., Ginzton, M., Rusinkiewicz, S., Pereira, L., Wood, D.: Scanalyze v1.0.3: A computer program for aligning and merging range data. Stanford Computer Graphics Laboratory, Stanford (2002)Google Scholar
  16. 16.
    Rugis, J.: Surface curvature maps and Michelangelo’s David. In: Proc. Image and Vision Computing, New Zealand, pp. 218–222 (2005)Google Scholar
  17. 17.
    Sun, C., Sherrah, J.: 3-D symmetry detection using the extended Gaussian image. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 164–168 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • John Rugis
    • 1
    • 2
  • Reinhard Klette
    • 1
  1. 1.CITR, Dep. of Computer ScienceThe University of AucklandAucklandNew Zealand
  2. 2.Dep. of Electrical & Computer EngineeringManukau Institute of TechnologyManukau CityNew Zealand

Personalised recommendations