https://github.com/QiujieDong/Laplacian2Mesh
Laplacian2Mesh: Laplacian-Based Mesh Understanding
https://github.com/QiujieDong/Laplacian2Mesh
geometric-deep-learning implicit-geodesic-connection laplacian-beltrami-space mesh-processing
Last synced: about 1 year ago
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Laplacian2Mesh: Laplacian-Based Mesh Understanding
- Host: GitHub
- URL: https://github.com/QiujieDong/Laplacian2Mesh
- Owner: QiujieDong
- License: mit
- Created: 2022-02-21T12:53:59.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-07-05T01:46:17.000Z (almost 2 years ago)
- Last Synced: 2024-08-01T03:42:00.034Z (almost 2 years ago)
- Topics: geometric-deep-learning, implicit-geodesic-connection, laplacian-beltrami-space, mesh-processing
- Language: Python
- Homepage: https://qiujiedong.github.io/publications/Laplacian2Mesh/
- Size: 7.61 MB
- Stars: 45
- Watchers: 7
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Laplacian2Mesh: Laplacian-Based Mesh Understanding
### [Project](https://qiujiedong.github.io/publications/Laplacian2Mesh/) | [Paper](https://arxiv.org/pdf/2202.00307.pdf)
**This repository is the official PyTorch implementation of our paper, *Laplacian2Mesh: Laplacian-Based Mesh Understanding*.**
## News
- :fire: This paper was accepted by [IEEE TVCG](https://ieeexplore.ieee.org/document/10076837)
- :star: Gave a talk at [CVM2023](http://iccvm.org/2023/index.htm) on Laplacian2Mesh.
## Requirements
- python 3.7
- CUDA 11.3
- Pytorch 1.10.0
To install other python requirements:
```
pip install -r requirements.txt
```
## Installation
clone this repo:
```
git clone https://github.com/QiujieDong/Laplacian2Mesh.git
cd Laplacian2Mesh
```
## Fetch Data
This repo provides training scripts for classification and segmentation on the following datasets:
- SHREC-11
- manifold40
- humanbody
- coseg_aliens
- coseg_chairs
- coseg_vases
To download the preprocessed data, run
```
sh ./scripts//get_data.sh
```
> The `coseg_aliens`, `coseg_chairs`, and `coseg_vases` are downloaded via the script of `coseg_aliens`.
> This repo uses the original `Manifold40` dataset without re-meshing via the Loop Subdivision.
## Preprocessing
To get the input features by preprocessing:
```
sh ./scripts//prepaer_data.sh
```
> The operation of preprocessing is one-time.
## Training
To train the model on the provided dataset(s) in this paper, run this command:
```
sh ./scripts//train.sh
```
> The training process is time-consuming, you can refer to [DiffusionNet](https://github.com/nmwsharp/diffusion-net) to optimize the code to speed up the training.
## Evaluation
To evaluate the model on a dataset, run:
```
sh ./scripts//test.sh
```
## Visualize
After testing the segmentation network, there will be colored shapes in the `visualization_result` directory.
## Cite
If you find our work useful for your research, please consider citing the following papers :)
```bibtex
@ARTICLE{Dong2023Laplacian2Mesh,
author={Dong, Qiujie and Wang, Zixiong and Li, Manyi and Gao, Junjie and Chen, Shuangmin and Shu, Zhenyu and Xin, Shiqing and Tu, Changhe and Wang, Wenping},
title={Laplacian2Mesh: Laplacian-Based Mesh Understanding},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2024},
volume={30},
number={7},
pages={4349-4361},
doi={10.1109/TVCG.2023.3259044}
}
```
## Acknowledgments
Our code is inspired by [MeshCNN](https://github.com/ranahanocka/MeshCNN/) and [SubdivNet](https://github.com/lzhengning/SubdivNet).