PPGNet

by svip-lab

svip-lab / PPGNet

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

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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

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.

pipe-line 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):
    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. (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}
}

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