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Segmentation in Point Clouds from RGB-D Using Spectral Graph Reduction

  • Margret KeuperEmail author
  • Thomas Brox
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

In this chapter, we tackle the problem of segmentation in point clouds from RGB-D data. In contrast to full point clouds, RGB-D data only provides a part of the volumetric information, the depth information of the one view given in the corresponding RGB image. Still, this additional information is valuable for the segmentation task as it helps disambiguating texture gradients from structure gradients. In order to create hierarchical segmentations, we combine a state-of-the-art method for natural RGB image segmentation based on spectral graph analysis with an RGB-D boundary detector. We show that spectral graph reduction can be employed in this case, facilitating the computation of RGB-D segmentations in large datasets.

Keywords

Point Cloud Image Segmentation Random Forest Spectral Cluster Image Patch 
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.

Notes

Acknowledgements

We acknowledge funding by the ERC Starting Grant VideoLearn.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of FreiburgFreiburg im BreisgauGermany

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