# AtlasNet

**Repository Path**: dang050/AtlasNet

## Basic Information

- **Project Name**: AtlasNet
- **Description**: This repository contains the source codes for the paper "AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation ". The network is able to synthesize a mesh (point cloud + connectivity) from a low-resolution point cloud, or from an image.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-25
- **Last Updated**: 2021-07-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## AtlasNet [[Project Page]](http://imagine.enpc.fr/~groueixt/atlasnet/) [[Paper]](https://arxiv.org/abs/1802.05384) [[Talk]](http://imagine.enpc.fr/~groueixt/atlasnet/atlasnet_slides_spotlight_CVPR.pptx)

**AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation** <br>
Thibault Groueix,  Matthew Fisher, Vladimir G. Kim , Bryan C. Russell, Mathieu Aubry  <br>
In [CVPR, 2018](http://cvpr2018.thecvf.com/).

:rocket: New branch : [AtlasNet + Shape Reconstruction by Learning Differentiable Surface Representations](https://github.com/ThibaultGROUEIX/AtlasNet/tree/jacobian_regularization)

<img src="doc/pictures/chair.png" alt="chair.png" width="35%" /> <img src="doc/pictures/chair.gif" alt="chair.gif" width="32%" />





### Install

This implementation uses Python 3.6, [Pytorch](http://pytorch.org/), [Pymesh](https://github.com/PyMesh/PyMesh), Cuda 10.1. 
```shell
# Copy/Paste the snippet in a terminal
git clone --recurse-submodules https://github.com/ThibaultGROUEIX/AtlasNet.git
cd AtlasNet 

#Dependencies
conda create -n atlasnet python=3.6 --yes
conda activate atlasnet
conda install  pytorch torchvision cudatoolkit=10.1 -c pytorch --yes
pip install --user --requirement  requirements.txt # pip dependencies
```



##### Optional : Compile Chamfer (MIT) + Metro Distance (GPL3 Licence)
```shell
# Copy/Paste the snippet in a terminal
python auxiliary/ChamferDistancePytorch/chamfer3D/setup.py install #MIT
cd auxiliary
git clone https://github.com/ThibaultGROUEIX/metro_sources.git
cd metro_sources; python setup.py --build # build metro distance #GPL3
cd ../..
```



### Usage

* **[Demo](./doc/demo.md)** :    ```python train.py --demo```
* **[Training](./doc/training.md)** :  ```python train.py --shapenet13```  *Monitor on  http://localhost:8890/*
* <details><summary> Latest Refacto 12-2019  </summary>
  - [x] Factorize Single View Reconstruction and autoencoder in same class <br>
  - [x] Factorise Square and Sphere template in same class<br>
  - [x] Add latent vector as bias after first layer(30% speedup) <br>
  - [x] Remove last th in decoder <br>
  - [x] Make large .pth tensor with all pointclouds in cache(drop the nasty Chunk_reader) <br>
  - [x] Make-it multi-gpu <br>
  - [x] Add netvision visualization of the results <br>
  - [x] Rewrite main script object-oriented  <br>
  - [x] Check that everything works in latest pytorch version <br>
  - [x] Add more layer by default and flag for the number of layers and hidden neurons <br>
  - [x] Add a flag to generate a mesh directly <br>
  - [x] Add a python setup install <br>
  - [x] Make sure GPU are used at 100% <br>
  - [x] Add f-score in Chamfer + report f-score <br>
  - [x] Get rid of shapenet_v2 data and use v1! <br>
  - [x] Fix path issues no more sys.path.append <br>
  - [x] Preprocess shapenet 55 and add it in dataloader <br>
  - [x] Make minimal dependencies <br>
  </details>

  

### Quantitative Results 


| Method                 | Chamfer (*1) | Fscore (*2) | [Metro](https://github.com/ThibaultGROUEIX/AtlasNet/issues/34) (*3) | Total Train time (min) |
| ---------------------- | ---- | ----   | ----- |-------     |
| Autoencoder 25 Squares | 1.35 | 82.3%   | 6.82  | 731       |
| Autoencoder 1 Sphere   | 1.35 | 83.3%   | 6.94  | 548    |
| SingleView 25  Squares | 3.78 | 63.1% | 8.94 | 1422      |
| SingleView 1 Sphere    | 3.76 | 64.4% |  9.01  | 1297      |


  * (*1) x1000. Computed between 2500 ground truth points and 2500 reconstructed points. 
  * (*2) The threshold is 0.001
  * (*3) x100. Metro is ran on unormalized point clouds (which explains a difference with the paper's numbers) 


### Related projects

*  [Learning Elementary Structures](https://github.com/TheoDEPRELLE/AtlasNetV2)
*  [3D-CODED](https://github.com/ThibaultGROUEIX/3D-CODED)
*  [Cycle Consistent Deformations](https://github.com/ThibaultGROUEIX/CycleConsistentDeformation)
*  [Atlasnet code V2.2](https://github.com/ThibaultGROUEIX/AtlasNet/tree/V2.2) (more linear, script like, may be easier to understand at first)





### Citing this work

```
@inproceedings{groueix2018,
          title={{AtlasNet: A Papier-M\^ach\'e Approach to Learning 3D Surface Generation}},
          author={Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan and Aubry, Mathieu},
          booktitle={Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
          year={2018}
        }
```
<p align="center">
  <img  src="doc/pictures/plane.gif">
</p>