update readme

README.md

@@ -1,11 +1,30 @@
-## Dynamic Graph CNN for Learning on Point Clouds
-This is the implementation of DGCNN and heavily based on  <a href="https://github.com/charlesq34/pointnet" target="_blank"> https://github.com/charlesq34/pointnet </a>
+# 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.
 
-### Classification
-1. python train.py
-2. After training finishes, python evalutate.py
+[[Project]](https://liuziwei7.github.io/projects/DGCNN) [[Paper]](https://arxiv.org/abs/1801.07829)     
 
-### Citation
+## 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. 
+
+<img src='./misc/demo_teaser.png' width=800>
+
+Further information please contact [Yue Wang](https://www.csail.mit.edu/person/yue-wang) and [Yongbin Sun](https://autoid.mit.edu/people-2).
+
+## Requirements
+* [TensorFlow](https://www.tensorflow.org/)
+
+## Point Cloud Classification
+* Run the training script:
+``` bash
+python train.py
+```
+* Run the evaluation script after training finished:
+``` bash
+python evalutate.py
+
+```
+
+## Citation
 Please cite this paper if you want to use it in your work,
 
 	@article{dgcnn,
@@ -15,5 +34,8 @@ Please cite this paper if you want to use it in your work,
 	  year={2018}
 	}
 
-### License
+## License
 MIT License
+
+## Acknowledgement
+This code is heavily borrowed from [PointNet](https://github.com/charlesq34/pointnet).