https://github.com/myavartanoo/PolyNet_PyTorch

Official implementation of the paper "PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation" (3DV 2021)
https://github.com/myavartanoo/PolyNet_PyTorch

deep-learning graphcnn graphconv pytorch

Last synced: about 2 months ago
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Official implementation of the paper "PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation" (3DV 2021)

• Host: GitHub
• URL: https://github.com/myavartanoo/PolyNet_PyTorch
• Owner: myavartanoo
• Created: 2021-10-06T08:24:37.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-08-26T04:03:50.000Z (about 2 years ago)
• Last Synced: 2024-07-21T21:43:16.155Z (2 months ago)
• Topics: deep-learning, graphcnn, graphconv, pytorch
• Language: Python
• Homepage:
• Size: 381 KB
• Stars: 18
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# PolyNet_Pytorch

This repository contains the official code to reproduce the results from the paper: 

**PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation (3DV 2021)**

\[[project page](https://myavartanoo.github.io/polynet/)\] \[[arXiv](https://arxiv.org/abs/2110.07882)\] \[[ResearchGate](https://www.researchgate.net/publication/355218072_PolyNet_Polynomial_Neural_Network_for_3D_Shape_Recognition_with_PolyShape_Representation)\]  \[[presentation](https://www.youtube.com/watch?v=Pk8gvfGV5N8&list=PLhCEMvtuQ92VkQCsiMKSn5ORcYM3BA1eM)\] 



  



### Dependencies

* Python 3.8.5

* PyTorch 1.7.1

* numpy

* Pillow

* torch_scatter

### Dataset

Download the preprocessed ModelNet dataset with PTQ and √3-subdivision from the follwing link and unzip them in the data directroy. The data type is ```.npz```.

\[[PTQ](https://drive.google.com/drive/folders/15VFhxRTpSfetJqNqssuNWNJhpHOBOUsE?usp=sharing)\] \[[√3-subdivision](https://drive.google.com/drive/folders/1WnwZ0NkSme9s_VceZRjVTpS1QYCJ4yYt?usp=sharing)\] 

### Train

In ```config.json``` you can set dataset type (ModelNet10 or ModelNet40) and the PolyPool type (PTQ, Sqrt3).

To train PolyNet with the desired dataset and PolyPool, simply run, 

```

CUDA_VISIBLE_DEVICES=0 python train.py --config config.json -t "direction to save the model"

```

## Citation

If you find our paper, code, or provided data useful, please consider citing:

```

@INPROCEEDINGS{9665897,

  author={Yavartanoo, Mohsen and Hung, Shih-Hsuan and Neshatavar, Reyhaneh and Zhang, Yue and Lee, Kyoung Mu},

  booktitle={2021 International Conference on 3D Vision (3DV)}, 

  title={PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation}, 

  year={2021},

  pages={1014-1023},

  doi={10.1109/3DV53792.2021.00109}}

```