# SubdivNet

**Repository Path**: boomabai/SubdivNet

## Basic Information

- **Project Name**: SubdivNet
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-06-18
- **Last Updated**: 2021-06-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Subdivision-based Mesh Convolutional Networks

The official implementation of `SubdivNet` in our paper,

[Subdivion-based Mesh Convolutional Networks](https://arxiv.org/abs/2106.02285)

![teaser](teaser.jpg)

## Requirements

* python3.7+
* CUDA 10.1+
* [Jittor](https://github.com/Jittor/jittor)

To install python requirements:

```setup
pip install -r requirements.txt
```

## Fetch Data
This repo provides training scripts for classification and segementation, 
on the following datasets,

- shrec11-split10
- shrec11-split16
- cubes
- manifold40 (based on ModelNet40)
- humanbody
- coseg-aliens

To download the preprocessed data, run

```
sh scripts/<DATASET_NAME>/get_data.sh
```

`Manfold40` (before remeshed) can be downloaded via [this link](https://cloud.tsinghua.edu.cn/f/2a292c598af94265a0b8/?dl=1).

## Training
To train the model(s) in the paper, run this command:

```
sh scripts/<DATASET_NAME>/train.sh
```

To speed up training, you can use multiple gpus. First install `OpenMPI`: 

```
sudo apt install openmpi-bin openmpi-common libopenmpi-dev
```

Then run the following command,

```
CUDA_VISIBLE_DEVICES="2,3" mpirun -np 2 sh scripts/<DATASET_NAME>/train.sh
```

## Evaluation

To evaluate the model on a dataset, run:

```
sh scripts/<DATASET_NAME>/test.sh
```

The pretrained weights are provided. Run the following command to download them.

```
sh scripts/<DATASET_NAME>/get_pretrained.sh
```

## Visualize
After testing the segmentation network, there will be colored shapes in a `results` directory. Use your favorite 3D viewer to check them.

## Apply to your own data
To create your own data with subdivision sequence connectivity, you may use the provided
tool that implements the MAPS algorithm. You may also refer to [NeuralSubdivision](https://github.com/HTDerekLiu/neuralSubdiv), as they provide a MATLAB scripts for remeshing.

To run our implemented MAPS algorithm, first install the following python dependecies,

```
triangle
pymeshlab
shapely
sortedcollections
networkx
rtree
```

Then see `datagen_maps.py` and modify the configurations to remesh your 3D shapes for subdivision connectivity.

## Cite
Please cite our paper if you use this code in your own work:

```
@misc{hu2021subdivisionbased,
      title={Subdivision-Based Mesh Convolution Networks}, 
      author={Shi-Min Hu and Zheng-Ning Liu and Meng-Hao Guo and Jun-Xiong Cai and Jiahui Huang and Tai-Jiang Mu and Ralph R. Martin},
      year={2021},
      eprint={2106.02285},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```