https://github.com/svip-lab/PPGNet

Source code for our CVPR 2019 paper - PPGNet: Learning Point-Pair Graph for Line Segment Detection
https://github.com/svip-lab/PPGNet

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Source code for our CVPR 2019 paper - PPGNet: Learning Point-Pair Graph for Line Segment Detection

• Host: GitHub
• URL: https://github.com/svip-lab/PPGNet
• Owner: svip-lab
• License: mit
• Created: 2019-04-04T07:40:01.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-07-29T04:29:30.000Z (over 5 years ago)
• Last Synced: 2024-08-03T01:15:14.687Z (3 months ago)
• Language: Python
• Homepage: https://www.aiyoggle.me/publication/ppgnet-cvpr19/
• Size: 94.7 KB
• Stars: 173
• Watchers: 13
• Forks: 37
• Open Issues: 12
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-image-alignment-and-stitching - **2019CVPR**

README

        
# PPGNet: Learning Point-Pair Graph for Line Segment Detection

PyTorch implementation of our CVPR 2019 paper:

[**PPGNet: Learning Point-Pair Graph for Line Segment Detection**](https://www.aiyoggle.me/publication/ppgnet-cvpr19/ppgnet-cvpr19.pdf)

Ziheng Zhang*, Zhengxin Li*, Ning Bi, Jia Zheng, Jinlei Wang, Kun Huang, Weixin Luo, Yanyu Xu, Shenghua Gao

(\* Equal Contribution)

The poster can be found [HERE](https://www.aiyoggle.me/publication/ppgnet-cvpr19).

![pipe-line](https://svip-lab.github.io/img/project/cvpr2019_zhangzh1.png)

**Demonstraton of juncton-line graph representaton G={V, E}. (a) an sample image patch with 10 junctons (V); (b) the graph which describes the connectvity of all junctons (G); (c) the adjacency matrix of all junctons (E, black means the junction pair is connected).** 

## Requirements

- Python >= 3.6

- fire >= 0.1.3

- numba >= 0.40.0

- numpy >= 1.14.5

- pytorch = 0.4.1

- scikit-learn = 0.19.2

- scipy = 1.1.0

- tensorboard >= 1.11.0

- tensorboardX >= 1.4

- torchvision >= 0.2.1

- OpenCV >= 3.4.3

## Usage

1. clone this repository (and make sure you fetch all .pth files right with [git-lfs](https://git-lfs.github.com/)): `git clone https://github.com/svip-lab/PPGNet.git`

2. download the preprocessed *SIST-Wireframe* dataset from [BaiduPan](https://pan.baidu.com/s/1Sbdi1lL492fhmPL1t1Ov0w) (code:lnfp) or [Google Drive](https://drive.google.com/file/d/1KggPcHCRu8BcOqCvVZCXiB64y9L2nQDf/view?usp=sharing).

3. specify the dataset path in the `train.sh` script. (modify the --data-root parameter)

4. run `train.sh`.

Please note that the code requires the GPU memory to be at least 24GB. For GPU with memory smaller than 24GB, you can use a smaller batch with `--batch-size` parameter and/or change the `--block-inference-size` parameter in `train.sh` to be a smaller integer to avoid the out-of-memory error.

## Citation

Please cite our paper for any purpose of usage.

```

@inproceedings{zhang2019ppgnet,

  title={PPGNet: Learning Point-Pair Graph for Line Segment Detection},

  author={Ziheng Zhang and Zhengxin Li and Ning Bi and Jia Zheng and Jinlei Wang and Kun Huang and Weixin Luo and Yanyu Xu and Shenghua Gao},

  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},

  year={2019}

}

```