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 is the author's re-implementation of Dynamic Graph CNN, which achieves state-of-the-art performance on point-cloud-related high-level tasks including category classification, semantic segmentation and part segmentation.

Further information please contact Yue Wang and Yongbin Sun.

Other Implementations

pytorch-dgcnn

Requirements

TensorFlow

Point Cloud Classification

Run the training script:

python train.py

Run the evaluation script after training finished:

python evalutate.py

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

Acknowledgement

This code is heavily borrowed from PointNet.

weiguochow/dgcnn