SkeletonNet

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Code and datasets for TPAMI 2021 "SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images "

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# SkeletonNet

This repository constains the codes and [ShapeNetV1-Surface-Skeleton](https://drive.google.com/file/d/1FlXiWFuBbryyNvyH07kGGl9WlmuYPVAP/view?usp=sharing),[ShapNetV1-SkeletalVolume](https://drive.google.com/file/d/1gmT6wF-wLYoa_CWfNsPYd0QtwW0V9NqB/view?usp=sharing) and 2d image datasets [ShapeNetRendering](http://cvgl.stanford.edu/data2/ShapeNetRendering.tgz).
Please download the above datasets at the first,  and then put them under the ```SkeletonNet/sharedata``` folder.

## Prepare Skeleton points/volumes

* If you want to use our skeletal point cloud extraction code, you can download the [skeleton extraction code](https://drive.google.com/file/d/1SGL8LJl1kgtUzM8_COwMMo-SzCPSpNLz/view?usp=sharing). This code is built on Visual Studio2013 + Qt. 
* If you want to convert the skeletal point clouds to skeletal volumes, you can run the below scripts.

```shell 
python sharedata/prepare_skeletalvolume.py --cats 03001627 --vx_res 32
python sharedata/prepare_skeletalvolume2.py --cats 03001627 --vx_res 64
python sharedata/prepare_skeletalvolume2.py --cats 03001627 --vx_res 128
python sharedata/prepare_skeletalvolume2.py --cats 03001627 --vx_res 256
```

Before running above scripts, you need to change ```raw_pointcloud_dir and upsample_skeleton_dir``` used when extracting skeletal points.

## Installation

First you need to create an anaconda environment called SkeletonNet using
```shell
conda env create -f environment.yaml
conda activate SkeletonNet
```

## Implementation details

For each stage, please refer to the README.md under the ```Skeleton_Inference/SkeGCNN/SkeDISN``` folder.

## Pre-trained models
  We provided pre-trained models of [SkeletonNet](https://drive.google.com/file/d/1WH0Nf30AWFOkBo0oWL9bzlNIWPvnwDiR/view?usp=sharing), [SkeGCNN](https://drive.google.com/file/d/1F7tTIMFyw-Yz6dTGRy3s1Aw_XfaRGUUa/view?usp=sharing), [SkeDISN](https://drive.google.com/file/d/1qMebY8qdbwCFRSTzJZoQ8T7sd9o4Mbu6/view?usp=sharing). 
  1. The pre-trained model of SkeletonNet should be put in the folder of ```./Skeleton_Inference/checkpoints/all```.
  2. The pre-trained model of SkeGCNN should be put in the folder of ```./SkeGCNN/checkpoint/skegcnn```.
  3. The pre-trained model of SkeDISN should be put in the folder of ```./SkeDISN/checkpoint/skedisn_occ```.

## Demo

1. use the SkeletonNet to generate base meshes or high-resolution volumes.
 ```
 cd Skeleton_Inference
 bash scripts/all/demo.sh
 cd ..
 ```

2. use the SkeGCNN to bridge the explicit mesh recovery via mesh deformations.
 ```
 cd SkeGCNN
 bash scripts/demo.sh
 cd ..
 ```

3. use the SkeDISN to regularize the implicit mesh recovery via skeleton local features.
 ```
 cd SkeDISN
 bash scripts/demo.sh
 cd ..
 ```

## Evalation

Please refer to the README.md under the ```./SkeDISN``` folder.

## Citation
If you find this work useful in your research, please consider citing:
```shell
@InProceedings{Tang_2019_CVPR,
author = {Tang, Jiapeng and Han, Xiaoguang and Pan, Junyi and Jia, Kui and Tong, Xin},
title = {A Skeleton-Bridged Deep Learning Approach for Generating Meshes of Complex Topologies From Single RGB Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@article{tang2020skeletonnet,
  title={SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images},
  author={Tang, Jiapeng and Han, Xiaoguang and Tan, Mingkui and Tong, Xin and Jia, Kui},
  journal={arXiv preprint arXiv:2008.05742},
  year={2020}
}
```

## Contact 
If you have any questions,  please feel free to contact with Tang Jiapeng msjptang@mail.scut.edu.cn or tangjiapengtjp@gmail.com

SkeletonNet

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