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.
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We acknowledge funding by the ERC Starting Grant VideoLearn.
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Keuper, M., Brox, T. (2016). Segmentation in Point Clouds from RGB-D Using Spectral Graph Reduction. In: Breuß, M., Bruckstein, A., Maragos, P., Wuhrer, S. (eds) Perspectives in Shape Analysis. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-24726-7_7
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DOI: https://doi.org/10.1007/978-3-319-24726-7_7
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