Further information and details please contact Guohao Li and Matthias Müller.

Requirements

• TensorFlow 1.12.0
• h5py
• vtk (only needed for visualization)
• jupyter notebook (only needed for visualization)

Conda Environment

In order to setup a conda environment with all neccessary dependencies run,

conda env create -f environment.yml

Getting Started

You will find detailed instructions how to use our code for semantic segmentation of 3D point clouds, in the folder sem_seg. Currently, we provide the following:

• Conda environment
• Setup of S3DIS Dataset
• Training code
• Evaluation code
• Several pretrained models
• Visualization code

Citation

Please cite our paper if you find anything helpful,

@InProceedings{li2019deepgcns,
    title={DeepGCNs: Can GCNs Go as Deep as CNNs?},
    author={Guohao Li and Matthias Müller and Ali Thabet and Bernard Ghanem},
    booktitle={The IEEE International Conference on Computer Vision (ICCV)},
    year={2019}
}

@misc{li2019deepgcns_journal,
    title={DeepGCNs: Making GCNs Go as Deep as CNNs},
    author={Guohao Li and Matthias Müller and Guocheng Qian and Itzel C. Delgadillo and Abdulellah Abualshour and Ali Thabet and Bernard Ghanem},
    year={2019},
    eprint={1910.06849},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

License

MIT License

Acknowledgement

This code is heavily borrowed from PointNet and EdgeConv. We would also like to thank 3d-semantic-segmentation for the visualization code.