PyTorch implementation of our CVPR 2019 paper:

PPGNet: Learning Point-Pair Graph for Line Segment Detection

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

(* Equal Contribution)

• Python >= 3.6
• fire >= 0.1.3
• numba >= 0.40.0
• numpy >= 0.15.0
• pytorch >= 0.4.1
• scikit-learn >= 0.19.1
• scipy >= 1.1.0
• tensorboard >= 1.11.0
• tensorboardX >= 1.4
• torchvision >= 0.2.1

1. clone this repository: git clone https://github.com/svip-lab/PPGNet.git
2. download the preprocessed SIST-Wireframe dataset from BaiduPan (code:lnfp) or Google Drive.
3. specify the dataset path in the train.sh script.
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.

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}
}