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Reduction of Point Cloud Artifacts Using Shape Priors Estimated with the Gaussian Process Latent Variable Model

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Pattern Recognition (GCPR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9796))

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Abstract

We present a method that removes point cloud artifacts like noisy points, missing data and outliers from a point cloud using a learned shape prior. The shape prior is learned with the Gaussian Process Latent Variable Model from a set of reference objects. As input data our method uses the estimated object pose from an object detector and a segmented point cloud. We show that the estimated shape prior is capable of modeling fine details to a certain degree. We also show that after applying our method the measured accuracy and completeness is increasing.

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Authors and Affiliations

  1. Computer Vision & Remote Sensing, TU Berlin, Berlin, Germany

    Jens Krenzin & Olaf Hellwich

Authors
  1. Jens Krenzin
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  2. Olaf Hellwich
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Corresponding authors

Correspondence to Jens Krenzin or Olaf Hellwich .

Editor information

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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Krenzin, J., Hellwich, O. (2016). Reduction of Point Cloud Artifacts Using Shape Priors Estimated with the Gaussian Process Latent Variable Model. 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_22

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  • DOI: https://doi.org/10.1007/978-3-319-45886-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45885-4

  • Online ISBN: 978-3-319-45886-1

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