{"id":13442804,"url":"https://github.com/QiujieDong/Laplacian2Mesh","last_synced_at":"2025-03-20T15:31:07.116Z","repository":{"id":112074137,"uuid":"461860945","full_name":"QiujieDong/Laplacian2Mesh","owner":"QiujieDong","description":"Laplacian2Mesh: Laplacian-Based Mesh 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src='assets/vase.gif' align=\"right\" width=200\u003e\n\n# Laplacian2Mesh: Laplacian-Based Mesh Understanding\n\n### [Project](https://qiujiedong.github.io/publications/Laplacian2Mesh/) | [Paper](https://arxiv.org/pdf/2202.00307.pdf)\n\n**This repository is the official PyTorch implementation of our paper,  *Laplacian2Mesh: Laplacian-Based Mesh Understanding*.**\n\n\u003cimg src='assets/teaser.png'\u003e\n\n## News\n- :fire: This paper was accepted by [IEEE TVCG](https://ieeexplore.ieee.org/document/10076837)\n- :star: Gave a talk at [CVM2023](http://iccvm.org/2023/index.htm) on Laplacian2Mesh.\n\n\n## Requirements\n\n- python 3.7\n- CUDA 11.3\n- Pytorch 1.10.0\n\nTo install other python requirements:\n```\npip install -r requirements.txt\n```\n\n## Installation\n\nclone this repo: \n\n```\ngit clone https://github.com/QiujieDong/Laplacian2Mesh.git\ncd Laplacian2Mesh\n```\n\n## Fetch Data\n\nThis repo provides training scripts for classification and segmentation on the following datasets:\n\n- SHREC-11\n- manifold40\n- humanbody\n- coseg_aliens\n- coseg_chairs\n- coseg_vases\n\nTo download the preprocessed data, run\n\n```\nsh ./scripts/\u003cDATASET_NAME\u003e/get_data.sh\n```\n\n\u003e The `coseg_aliens`, `coseg_chairs`, and `coseg_vases` are downloaded via the script of `coseg_aliens`.\n\u003e This repo uses the original `Manifold40` dataset without re-meshing via the Loop Subdivision.\n\n## Preprocessing\n\nTo get the input features by preprocessing:\n\n```\nsh ./scripts/\u003cDATASET_NAME\u003e/prepaer_data.sh\n```\n\n\u003e The operation of preprocessing is one-time.\n\n## Training\n\nTo train the model on the provided dataset(s) in this paper, run this command:\n\n```\nsh ./scripts/\u003cDATASET_NAME\u003e/train.sh\n```\n\n\u003e 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.\n\n## Evaluation\n\nTo evaluate the model on a dataset, run:\n\n```\nsh ./scripts/\u003cDATASET_NAME\u003e/test.sh\n```\n\n## Visualize\n\nAfter testing the segmentation network, there will be colored shapes in the `visualization_result` directory.\n\n## Cite\n\nIf you find our work useful for your research, please consider citing the following papers :)\n\n```bibtex\n@ARTICLE{Dong2023Laplacian2Mesh,\n    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},\n    title={Laplacian2Mesh: Laplacian-Based Mesh Understanding},\n    journal={IEEE Transactions on Visualization and Computer Graphics},\n    year={2024},\n    volume={30},\n    number={7},\n    pages={4349-4361},\n    doi={10.1109/TVCG.2023.3259044}\n}\n```\n\n\n## Acknowledgments\nOur code is inspired by [MeshCNN](https://github.com/ranahanocka/MeshCNN/) and [SubdivNet](https://github.com/lzhengning/SubdivNet).\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FQiujieDong%2FLaplacian2Mesh","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FQiujieDong%2FLaplacian2Mesh","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FQiujieDong%2FLaplacian2Mesh/lists"}