Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

@article{Pagnutti2017SegmentationAS,
  title={Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting},
  author={Giampaolo Pagnutti and L. Minto and Pietro Zanuttigh},
  journal={IET Comput. Vis.},
  year={2017},
  volume={11},
  pages={633-642}
}
We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax… 
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