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Extracting and Visualizing Structural Features in Environmental Point Cloud LiDaR Data Sets

  • Patric Keller
  • Oliver Kreylos
  • Marek Vanco
  • Martin Hering-Bertram
  • Eric S. Cowgill
  • Louise H. Kellogg
  • Bernd Hamann
  • Hans Hagen
Chapter
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

We present a user-assisted approach to extracting and visualizing structural features from point clouds obtained by terrestrial and airborne laser scanning devices. We apply a multi-scale approach to express the membership of local point environments to corresponding geometric shape classes in terms of probability. This information is filtered and combined to establish feature graphs which can be visualized in combination with the color-encoded feature and structural probability estimates of the measured raw point data. Our method can be used, for example, for exploring geological point data scanned from multiple viewpoints.

Keywords

Feature Extraction Point Cloud LiDaR Data Feature Graph Move Little Square 
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.

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Notes

Acknowledgements

This work was supported by the DFG’s International Research Training Group (IRTG) 1131 at the University of Kaiserslautern and the Center for Mathematical and Computational Modeling (CM)2 It was also supported in part by the W.M. Keck Foundation through the UC Davis Center for Active Visualization in the Earth Sciences (KeckCAVES), Department of Geology. We thank the members of the Visualization and Computer Graphics Research Group at the Institute for Data Analysis and Visualization (IDAV) at UC Davis. We thank Dr. Gerald Bawden of the US Geological Survey for sharing tripod LiDaR data.

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Copyright information

© Springer Berlin Heidelberg 2011

Authors and Affiliations

  • Patric Keller
    • 1
  • Oliver Kreylos
    • 2
  • Marek Vanco
    • 2
  • Martin Hering-Bertram
    • 3
  • Eric S. Cowgill
    • 4
  • Louise H. Kellogg
    • 4
  • Bernd Hamann
    • 2
  • Hans Hagen
    • 1
  1. 1.Department of CS.University of KaiserslauternKaiserslauternGermany
  2. 2.Department of Computer ScienceInstitute for Data Analysis and Visualization (IDAV)UC DavisUSA
  3. 3.Fraunhofer-Institute ITWMKaiserslauternGermany
  4. 4.Department of GeologyUniversity of CaliforniaUC DavisUSA

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