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] [Press]

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.

Author's Implementations

The classification experiments in our paper are done with the pytorch implementation.

• tensorflow-dgcnn
• pytorch-dgcnn

Other Implementations

• pytorch-geometric
• pytorch-dgcnn (This implementation on S3DIS achieves significant better results than our tensorflow implementation)

Generalization under Corruptions

The performance is evaluated on ModelNet-C with mCE (lower is better) and clean OA (higher is better).

Method	Reference	Standalone	mCE	Clean OA
PointNet	Qi et al.	Yes	1.422	0.907
DGCNN	Wang et al.	Yes	1.000	0.926

Real-World Applications

• DGCNN has been successfully applied to ParticalNet in Large Hadron Collider (LHC).

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

The structure of this codebase is borrowed from PointNet.