Dynamic Graph CNN for Learning on Amino Acid Point Cloud Structure

We use a neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds including classification.

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. We build upon this work and configure this implementation for amino acid structure recognition.

Requirements

• TensorFlow

Amino Acid Point Cloud Classification

• Run the training script:

python train.py

• Run the evaluation script after training finished:

python evalutate.py

Citation

@article{dgcnn,
  title={Dynamic Graph CNN for Learning on Point Clouds},
  author={Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon},
  journal={arXiv preprint arXiv:1801.07829},
  year={2018}
}

Reference: [[Paper]](https://arxiv.org/abs/1801.07829)     

License

MIT License

Acknowledgement

This code is heavily borrowed from PointNet.