TMNet

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This repository contains the code for the paper "Deep Mesh Reconstruction from Single RGB Images ".

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# TMNet
This repository contains the source codes for the paper 
[Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks](https://arxiv.org/abs/1909.00321).

### Citing this work

If you find this work useful in your research, please consider citing:

```
@inproceedings{pan2019deep,
  title={Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks},
  author={Pan, Junyi and Han, Xiaoguang and Chen, Weikai and Tang, Jiapeng and Jia, Kui},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={9964--9973},
  year={2019}
}
```
# Install

### Clone the repo and install dependencies

This implementation uses [Pytorch 1.0.0](http://pytorch.org/).

```shell
## Download the repository
git clone https://github.com/jnypan/TMNet.git
cd TMNet
## Create python env with relevant packages
conda create --name tmnet python=3.7
source activate tmnet
pip install pandas visdom
cd ./extension
python setup.py install
```

# Download

### Data

We used the [ShapeNet](https://www.shapenet.org/) dataset for 3D models, and rendered views from [3D-R2N2](https://github.com/chrischoy/3D-R2N2):

When using the provided data make sure to respect the shapenet [license](https://shapenet.org/terms).

* [The ShapeNet point clouds (*.mat)](https://drive.google.com/file/d/1Z0d8W4PJnWIoCqt1jM4ziSFd1tgBUHa6/view?usp=sharing) go in ``` data/customShapeNet_mat```
* [the rendered views (*.png)](https://drive.google.com/file/d/1eu2-Qm6T9AhjDkKP6IY-G__ti1N37VBr/view?usp=sharing) go in ``` data/ShapeNetRendering```

# Usage

### Train
```
bash train/train.sh
```

### Generate
```
python generate.py --model $model_path
```

# Evaluation results
The evaluation is based on 2,048 ground truth points and 2,048 points [uniformly sampled](http://rodolphe-vaillant.fr/?e=37) from the generated meshes. The CD is multiplied by 1,000 and the EMD is multiplied by 100. The threshold for F-score is
set to be 0.002.

| Chair | CD |EMD |F-score|
| ---------- | --------------------- | --------------------- | --------------------- |
| OccNet     | 6.647               | 13.266  |69.08|
| AtlasNet | 5.415 | 12.106 |70.51|
| Ours     | 4.850 |11.256|73.72|

TMNet

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