a pytorch implimentation of Dynamic Graph CNN(EdgeConv)

I impliment the classfication network in the paper, and only the vanilla version. DGCNN(Dynamic Graph CNN) is based on the architecture of PointNet to do a point cloud classification task or a segmentation task.

To train the model, just set the path of you ModelNet40 dataset(you can download it from here) in dataset.py.

Run: python dataset.py, without any error printed. If does, please check the path of your dataset.

If you want to change the hyper-parameters of the model, you can modify params.py yourserf.

Then, just simply runing: python train.py, and it will start training. The training procedure would be saved in a directory called summary , and the model weights would be saved in a directory called weights.

The classification accuracy on test set is 91.2% on ModelNet40 dataset. And my training result is here:

• Dynamic Graph CNN for Learning on Point Clouds

• ModelNet40