Update README.md

README.md

@@ -1,14 +1,15 @@
 # 3DIAS_Pytorch
-[project page](https://myavartanoo.github.io/3dias/) [arXiv](https://arxiv.org/abs/2108.08653) [ICCV]()
-
-![Example 1](source/airplane.gif)
-![Example 2](source/chair1.gif)
-![Example 3](source/lamp.gif)
-![Example 4](source/speaker.gif)
-
 This repository contains the official code to reproduce the results from the paper: 
+
 **3DIAS: 3D Shape Reconstruction with Implicit Algebraic Surfaces (ICCV 2021)**
 
+\[[project page](https://myavartanoo.github.io/3dias/)\] \[[arXiv](https://arxiv.org/abs/2108.08653)\] \[[ICCV]()\]
+
+\\<!-- ![Example 1](source/airplane.gif) -->
+<img src="source/chair1.gif" width="30%" height="30%"/>
+<img src="source/lamp.gif" width="30%" height="30%"/>
+<img src="source/speaker.gif" width="30%" height="30%"/>
+
 
 ## Installation
 TBD
@@ -18,14 +19,18 @@ save `config.json` and `checkpoint.pth` in `weigths` folder
 
 
 ## Demo
-![Example Input](source/example_input.png)
+\\<!-- ![Example Input](source/example_input.png) -->
 
 You can now test our code on the provided input images in the `input` folder.
 To this end, simply run `sh run.sh` or, 
 ```
 CUDA_VISIBLE_DEVICES=0 python demo.py --config "./weights/config.json" --resume "./weights/checkpoint-epoch890.pth" --inputimg "./input/03001627_ef4e47e54bfc685cb40f0ac0fb9a650d_14.png"
 ```
 
+You can check the output mesh in `output` folder. (We have created an example mesh)
+* total.ply is a whole mesh
+* parts_<number>.ply are meshes for parts
+
 
 ## Dataset
 TBD
@@ -37,12 +42,12 @@ TBD
 TBD
 
 
-# Citation
+## Citation
 If you find our code or paper useful, please consider citing
 
     @inproceedings{Occupancy Networks,
         title = {Occupancy Networks: Learning 3D Reconstruction in Function Space},
         author = {Mescheder, Lars and Oechsle, Michael and Niemeyer, Michael and Nowozin, Sebastian and Geiger, Andreas},
         booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
         year = {2019}
-    }
+    }