update README.md

README.md

@@ -1,12 +1,22 @@
 # PSVH-3d-reconstruction
-This repository is the implementation of our AAAI19 paper [Deep Single-View 3D Object Reconstruction with Visual Hull Embedding](https://arxiv.org/pdf/1809.03451.pdf). 
+This repository is the implementation of our AAAI 2019 paper:
 
-The authors of this paper are [Hanqing Wang](https://qweas120.github.io), [Jiaolong Yang](http://jlyang.org/), [Wei Liang](http://iitlab.bit.edu.cn/mcislab/~liangwei/) and [Xin Tong](http://www.xtong.info/).
+[Deep Single-View 3D Object Reconstruction with Visual Hull Embedding](https://arxiv.org/pdf/1809.03451.pdf)
 
+[Hanqing Wang](https://qweas120.github.io), [Jiaolong Yang](http://jlyang.org/), [Wei Liang](http://iitlab.bit.edu.cn/mcislab/~liangwei/), [Xin Tong](http://www.xtong.info/)
+
+This work is implemented using [TensorFlow](https://www.tensorflow.org/).
 ## Introduction
+In this paper, we present an approach which aims to preserve more shape details and improve the reconstruction quality. The key idea of our method is to lever- age object mask and pose estimation from CNNs to assist the 3D shape learning by constructing a probabilistic single-view visual hull inside of the network. 
+
+<img src="./readme/img/network.png">
 
+Our method works by first predicting a coarse shape as well as the object pose and silhouette using CNNs, followed by a novel 3D refinement CNN which refines the coarse shapes using the constructed probabilistic visual hulls.
 <!-- ![image](./readme/img/results/result.png) -->
-<img src="./readme/img/results/result.png" align="center" width="65%">
+<!-- <img src="./readme/img/results/result.png" align="center" width="65%"> -->
+
+## Examples
+<img src="./readme/img/results/result.png" align="center" width="100%">
 
 ## Citation
 If you find our work helpful for your research, please cite our paper:
@@ -31,11 +41,13 @@ conda install tensorflow pillow
 ```
 The checkpoint of the trained models are available [here](https://drive.google.com/open?id=1TJEUUhmZL8WJgQbsrRX9D_GKAiqE8Gic)(426MB). Extract the files to the root directory.
 
-## Run Example
+## Demo
 
 Run `python run_case.py` to run the examples. The outputs are reconstruction results before and after the refinement (Please refer to our paper for more details). The results are in `obj` format. You can use [meshlab](http://www.meshlab.net/) for visulization.    
 
 ## Acknowlegement
 
 
 
+## License
+PSVH is freely available for non-commercial use, and may be redistributed under these conditions. Please see the license for further details. For commercial license, please contact the authors.