Fast Marching for Robust Surface Segmentation

  • Falko Schindler
  • Wolfgang Förstner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6952)

Abstract

We propose a surface segmentation method based on Fast Marching Farthest Point Sampling designed for noisy, visually reconstructed point clouds or laser range data. Adjusting the distance metric between neighboring vertices we obtain robust, edge-preserving segmentations based on local curvature. We formulate a cost function given a segmentation in terms of a description length to be minimized. An incremental-decremental segmentation procedure approximates a global optimum of the cost function and prevents from under- as well as strong over-segmentation. We demonstrate the proposed method on various synthetic and real-world data sets.

Keywords

Cost Function Point Cloud Segmentation Result Voronoi Diagram Seed Point 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Falko Schindler
    • 1
  • Wolfgang Förstner
    • 1
  1. 1.Department of PhotogrammetryUniversity of BonnBonnGermany

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