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Contiguous Patch Segmentation in Pointclouds

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Pattern Recognition (GCPR 2016)

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Abstract

An algorithm for Contiguous PAtch Segmentation (CPAS) in 3D pointclouds is proposed. In contrast to current state-of-the-art algorithms, CPAS is robust, scalable and provides a more complete description by simultaneously detecting contiguous patches as well as delineating object boundaries. Our algorithm uses a voxel grid to divide the scene into non-overlapping voxels within which clipped planes are fitted with RANSAC. Using a Dirichlet process mixture (DPM) model of Gaussians and connected component analysis, voxels are clustered into contiguous regions. Finally, we use importance sampling on the convex-hull of each region to obtain the underlying patch and object boundary estimates. For urban scenes, the segmentation represents building walls, ground and roof elements (Fig. 1). We demonstrate the robustness of CPAS using data sets from both image matching and raw LiDAR scans.

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Authors and Affiliations

  1. Institute of Applied Computer Science, Bundeswehr University Munich, Neubiberg, Germany

    William Nguatem & Helmut Mayer

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  1. William Nguatem
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  2. Helmut Mayer
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Correspondence to William Nguatem or Helmut Mayer .

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Editors and Affiliations

  1. University of Hannover, Hannover, Germany

    Bodo Rosenhahn

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Bjoern Andres

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Nguatem, W., Mayer, H. (2016). Contiguous Patch Segmentation in Pointclouds. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-45886-1_11

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