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https://github.com/imoonlab/pvrnet
PVRNet: Point-View Relation Neural Network for 3D Shape Recognition (AAAI 2019)
https://github.com/imoonlab/pvrnet
3d-shape-recognition deep-learning multi-view point-cloud pytorch
Last synced: 30 days ago
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PVRNet: Point-View Relation Neural Network for 3D Shape Recognition (AAAI 2019)
- Host: GitHub
- URL: https://github.com/imoonlab/pvrnet
- Owner: iMoonLab
- License: mit
- Created: 2019-07-14T12:23:46.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-07-15T02:17:57.000Z (over 5 years ago)
- Last Synced: 2024-10-28T06:58:35.912Z (about 2 months ago)
- Topics: 3d-shape-recognition, deep-learning, multi-view, point-cloud, pytorch
- Language: Python
- Size: 206 KB
- Stars: 46
- Watchers: 4
- Forks: 8
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# PVRNet
PVRNet: Point-View Relation Neural Network for 3D Shape Recognition (AAAI 2019)
Created by Haoxuan You, Yifan Feng, Xibin Zhao, Changqing Zou, Rongrong Ji, Yue Gao from Tsinghua University.
### Introduction
This work will appear in AAAI 2019. We propose a point-view relation neural network called PVRNet for 3D shape recognition and retrieval. You can chekc our [paper](https://arxiv.org/abs/1812.00333) for more details.
In this repository, our code and data are released for training our PVRNet on ModelNet40 dataset.
### Citation
If you find our work useful in your research, please cite our paper:
```
@article{you2018pvrnet,
title={PVRNet: Point-View Relation Neural Network for 3D Shape Recognition},
author={You, Haoxuan and Feng, Yifan and Zhao, Xibin and Zou, Changqing and Ji, Rongrong and Gao, Yue},
journal={AAAI 2019},
year={2018}
}
```
### Configuration
Code is tested under the environment of Pytorch 0.4.1, Python 3.6 and CUDA 9.0 on Ubuntu 16.04.
Data: [point cloud data](https://drive.google.com/file/d/1DUh_8PQjh3ds4yO0O8q_vb0HPistOJ4y/view?usp=sharing) and [multi-view(12-view) data](https://drive.google.com/file/d/12JbIPLvcSUsMjxb_CZYXI8xQK2UKosio/view?usp=sharing) from ModelNet40 dataset.
Pretrained Model: [multi-view part(MVCNN)](https://drive.google.com/file/d/1dZG7XojtPS9Cl5aaH4iWXA_N2PximB6i/view?usp=sharing), [point cloud part(DGCNN)](https://drive.google.com/file/d/1fY9E44xuPwUFxJ_BIeP5NXwrB7DQm1tw/view?usp=sharing) and [PVRNet](https://drive.google.com/file/d/1g3Ef68jRSY2mNf54dOeqNFYZTm4cO13d/view?usp=sharing)
### Usage
+ Download data and pretrained ckpt from above links. Create dir for data as well as result, and place them under corresponding dirs(./data/ and ./result/ckpt/).
```mkdir -p data result/ckpt```
+ Train PVRNet. This would use pretrained MVCNN model and DGCNN model saved in ./result/ckpt:
``` python train_pvrnet.py```
+ If validate the performance of PVRNet with our pretrained model:
`python val_pvrnet.py`
If validate the performance of pretrained MVCNN and DGCNN models:
```
python val_mvcnn.py
python val_pc.py
```
+ If you want to train new model for MVCNN and DGCNN:
```
python train_mvcnn.py
python train_pc.py
```
### License
Our code is released under MIT License (see LICENSE file for details).