packyan/DGCNN-Pytorch

A Re-Implement of Dynamic Graph CNN for Point-Cloud Classification and Segmentation

DGCNN-Pytorch

A Re-Implement of Dynamic Graph CNN for Point-Cloud Classification and Segmentation

Dynamic Graph CNN for Learning on Point Clouds

We propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds including classification and segmentation. EdgeConv is differentiable and can be plugged into existing architectures.

[Project] [Paper]

Overview

DGCNN-Pytorch is my personal re-implementation of Dynamic Graph CNN.

Run

Point-Cloud Data Preparations

There is two ways to convert ModelNet40 PLY or OFF file to Point-Cloud.

1. Use h5_dataloader.py download and load modelnet40_ply_hdf5_2048 files

2. Custom down-sampling points from mesh. Download Modelnet40 off file, and unzip it in Data/ModelNet40 Run Sampler with test = 0 and test = 1, and sampled point-cloud file will save in ModelNet40_ Next run pointcloud_dataloader to convert *.points to h5 file. Data\ModelNet40_ folder will create ModelNet40_test.h5 and ModelNet40_train.h5

Train

Train model: Run train to train your model. Now is PointNet, next will update to pointnet and DGCNN.

To-Do

Next few days, will upload DGCNN model.

Citation

Please cite this paper if you want to use it in your work,

@article{dgcnn,
  title={Dynamic Graph CNN for Learning on Point Clouds},
  author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
  journal={ACM Transactions on Graphics (TOG)},
  year={2019}
}

License

MIT License