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.
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.
- Run the training script:
python train.py- Run the evaluation script after training finished:
python evalutate.py
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={Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon},
journal={arXiv preprint arXiv:1801.07829},
year={2018}
}
MIT License
This code is heavily borrowed from PointNet.