Abstract

In this paper we present a new approach for semantic recognition in the context of robotics. When a robot evolves in its environment, it gets 3D information given either by its sensors or by its own motion through 3D reconstruction. Our approach uses (i) 3D-coherent synthesis of scene observations and (ii) mix them in a multi-view framework for 3D labeling. (iii) This is efficient locally (for 2D semantic segmentation) and globally (for 3D structure labeling). This allows to add semantics to the observed scene that goes beyond simple image classification, as shown on challenging datasets such as SUNRGBD or the 3DRMS Reconstruction Challenge.

Citation

@inproceedings{guerry2017snapnet,
  title={Snapnet-r: Consistent 3d multi-view semantic labeling for robotics},
  author={Guerry, Joris and Boulch, Alexandre and Le Saux, Bertrand and Moras, Julien and Plyer, Aur{\'e}lien and Filliat, David},
  booktitle={Proceedings of the IEEE international conference on computer vision workshops},
  pages={669--678},
  year={2017}
}