SnapNet: Unstructured point cloud semantic labeling using deep segmentation networks

Published in Computer and Graphics, 2017

Authors: Alexandre Boulch, Bertrand Le Saux and Nicolas Audebert

Code available at Github

Computer and Graphics version

Link to the paper.

@article{boulch2017snapnet,
  title={SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks},
  author={Boulch, Alexandre and Guerry, Joris and Le Saux, Bertrand and Audebert, Nicolas},
  journal={Computers \& Graphics},
  year={2017},
  publisher={Elsevier}
}

3DOR Version

A preliminary version of the this work has been presented at 10th Eurographics workshop on 3D Object retrieval, 3DOR 2017.

Download paper here

@inproceedings{boulch2017unstructured,
  title={Unstructured point cloud semantic labeling using deep segmentation networks},
  author={Boulch, Alexandre and Saux, Bertrand Le and Audebert, Nicolas},
  booktitle={Eurographics Workshop on 3D Object Retrieval},
  volume={2},
  year={2017}
}

CODE

The code is available in the dedicated Github repository.

The pretrained weights of VGG16, from the caffe model are available here

The network weights are available to download:

RGB model

Composite model

Fusion model (to be used with RGB and Composite)