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https://github.com/lightaime/deep_gcns
Tensorflow Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?" ICCV2019 Oral https://www.deepgcns.org
https://github.com/lightaime/deep_gcns
3d-point-clouds deep-gcns geometric-deep-learning graph-neural-networks
Last synced: 19 days ago
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Tensorflow Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?" ICCV2019 Oral https://www.deepgcns.org
- Host: GitHub
- URL: https://github.com/lightaime/deep_gcns
- Owner: lightaime
- License: mit
- Created: 2019-04-05T23:52:51.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-01-23T13:17:32.000Z (almost 5 years ago)
- Last Synced: 2024-08-01T03:45:57.616Z (4 months ago)
- Topics: 3d-point-clouds, deep-gcns, geometric-deep-learning, graph-neural-networks
- Language: Python
- Homepage:
- Size: 219 MB
- Stars: 628
- Watchers: 20
- Forks: 89
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-gcn - lightaime/deep_gcns
README
# DeepGCNs: Can GCNs Go as Deep as CNNs?
In this work, we present new ways to successfully train very deep GCNs. We borrow concepts from CNNs, mainly residual/dense connections and dilated convolutions, and adapt them to GCN architectures. Through extensive experiments, we show the positive effect of these deep GCN frameworks.[[Project]](https://www.deepgcns.org/) [[Paper]](https://arxiv.org/abs/1904.03751) [[Slides]](https://docs.google.com/presentation/d/1L82wWymMnHyYJk3xUKvteEWD5fX0jVRbCbI65Cxxku0/edit?usp=sharing) [[Tensorflow Code]](https://github.com/lightaime/deep_gcns) [[Pytorch Code]](https://github.com/lightaime/deep_gcns_torch)
## Overview
We do extensive experiments to show how different components (#Layers, #Filters, #Nearest Neighbors, Dilation, etc.) effect `DeepGCNs`. We also provide ablation studies on different type of Deep GCNs (MRGCN, EdgeConv, GraphSage and GIN).
Further information and details please contact [Guohao Li](https://ghli.org) and [Matthias Müller](https://matthias.pw/).
## Requirements
* [TensorFlow 1.12.0](https://www.tensorflow.org/)
* [h5py](https://www.h5py.org/)
* [vtk](https://vtk.org/) (only needed for visualization)
* [jupyter notebook](https://jupyter.org/) (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](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](https://github.com/charlesq34/pointnet) and [EdgeConv](https://github.com/WangYueFt/dgcnn). We would also like to thank [3d-semantic-segmentation](https://github.com/VisualComputingInstitute/3d-semantic-segmentation) for the visualization code.