Persistent Point Feature Histograms for 3D Point Clouds (bibtex)
by Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, Michael Beetz
Abstract:
This paper proposes a novel way of characterizing the local geometry of 3D points, using persistent feature histograms. The relationships between the neighbors of a point are analyzed and the resulted values are stored in a 16-bin histogram. The histograms are pose and point cloud density invariant and cope well with noisy datasets. We show that geometric primitives have unique signatures in this feature space, preserved even in the presence of additive noise. To extract a compact subset of points which characterizes a point cloud dataset, we perform an in-depth analysis of all point feature histograms using different distance metrics. Preliminary results show that point clouds can be roughly segmented based on the uniqueness of geometric primitives feature histograms. We validate our approach on datasets acquired. from laser sensors in indoor (kitchen) environments.
Reference:
Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, Michael Beetz, "Persistent Point Feature Histograms for 3D Point Clouds", In Proceedings of the 10th International Conference on Intelligent Autonomous Systems (IAS-10), Baden-Baden, Germany, 2008.
Bibtex Entry:
@InProceedings{Rusu08IAS,
  author    = {Radu Bogdan Rusu and Zoltan Csaba Marton and Nico Blodow and Michael Beetz},
  title     = {{Persistent Point Feature Histograms for 3D Point Clouds}},
  booktitle = {Proceedings of the 10th International Conference on Intelligent Autonomous Systems (IAS-10), Baden-Baden, Germany},
  year      = {2008},
  abstract  = {
               This paper proposes a novel way of characterizing the local geometry of 3D
               points, using persistent feature histograms. The relationships between the
               neighbors of a point are analyzed and the resulted values are stored in a
               16-bin histogram. The histograms are pose and point cloud density invariant
               and cope well with noisy datasets. We show that geometric primitives have
               unique signatures in this feature space, preserved even in the presence of
               additive noise. To extract a compact subset of points which characterizes a
               point cloud dataset, we perform an in-depth analysis of all point feature
               histograms using different distance metrics. Preliminary results show that
               point clouds can be roughly segmented based on the uniqueness of geometric
               primitives feature histograms. We validate our approach on datasets acquired.
               from laser sensors in indoor (kitchen) environments.
  },
  bib2html_pubtype = {Conference Paper},
  bib2html_rescat  = {Perception},
  bib2html_groups  = {EnvMod},
  bib2html_funding = {CoTeSys},
  bib2html_domain  = {Assistive Household},
}
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