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Contiguous Patch Segmentation in Pointclouds

  • William NguatemEmail author
  • Helmut MayerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)

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.

Keywords

Dirichlet Process Contiguous Region Indoor Scene Connected Component Analysis Dirichlet Process Mixture 
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 International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Applied Computer ScienceBundeswehr University MunichNeubibergGermany

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