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Holistically-Attracted Wireframe Parsing [TPAMI'23] & [CVPR' 20]

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HAWP (Experimental branch for inference-only usage)

[News] We are refactorizing our HAWP as a python package for inference.

Highlights

  • We propose a fast and parsimonious parsing method HAWP to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass.
  • The proposed HAWP is fully end-to-end.
  • The proposed HAWP does not require the squeeze module.
  • State-of-the-art performance on the Wireframe dataset and YorkUrban dataset.
  • The proposed HAWP achieves 29.5 FPS on a GPU (Tesla V100) for 1-batch inference.

Installation

  1. Install PyTorch from https://pytorch.org/get-started/locally/
  2. Install hawp to your python environment by
pip install git+https://github.com/cherubicXN/hawp.git@inference

Usage

python -m hawp.predict [options] images

Help

python -m hawp.predict --help
usage: python -m hawp.predict [options] images

positional arguments:
  images                input images (default: None)

optional arguments:
  -h, --help            show this help message and exit
  --glob GLOB           glob expression for input images (for many images)
                        (default: None)
  -o [IMAGE_OUTPUT], --image-output [IMAGE_OUTPUT]
                        Whether to output an image, with the option to specify
                        the output path or directory (default: None)
  --json-output [JSON_OUTPUT]
                        Whether to output a json file, with the option to
                        specify the output path or directory (default: None)
  --disable-cuda        disable CUDA (default: False)

show:
  --show                show every plot, i.e., call matplotlib show()
                        (default: False)
  --edge-threshold EDGE_THRESHOLD
                        show the wireframe edges whose confidences are greater
                        than [edge_threshold] (default: None)
  --out-ext OUT_EXT     save the plot in specific format (default: png)

Citations

If you find our work useful in your research, please consider citing:

@inproceedings{HAWP,
title = "Holistically-Attracted Wireframe Parsing",
author = "Nan Xue and Tianfu Wu and Song Bai and Fu-Dong Wang and Gui-Song Xia and Liangpei Zhang and Philip H.S. Torr
",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
year = {2020},
}

Acknowledgment

  • We acknowledge the effort from the authors of the Wireframe dataset and the YorkUrban dataset. These datasets make accurate line segment detection and wireframe parsing possible.

  • This branch is partially based on openpifpaf.

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Holistically-Attracted Wireframe Parsing [TPAMI'23] & [CVPR' 20]

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