PLNet

This is the official implementation of PLNet, which is the feature detector of our AirSLAM. PLNet is a convolutional neural network (CNN) designed to simultaneously detect keypoints and structural lines. It leverages a shared backbone and specialized headers for keypoint and line detection. The shared backbone design makles PLNet highly efficient.

• The training and testing data (including Wireframe dataset and YorkUrban dataset) can be downloaded via Google Drive. Many thanks to authors of these two excellent datasets!

• You can also use the gdown to download the data in the terminal by

  gdown 134L-u9pgGtnzw0auPv8ykHqMjjZ2claO
  unzip data.zip

• Clone the code repo: git clone https://github.com/sair-lab/PLNet.git.
• Install ninja-build by sudo apt install ninja-build.
• Create a conda environment by

conda create -n plnet python==3.9
conda activate plnet
pip install -e .

• Run the following command lines to install the dependencies of PLNet

# Install pytorch, please be careful for the version of CUDA on your machine
pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116 
# Install other dependencies
pip install -r requirement.txt

• Verify the installation.

python -c "import torch; print(torch.cuda.is_available())" # Check if the installed pytorch supports CUDA.

We also provide a Dockerfile. You could build the docker image by running the following command lines.

sudo docker build - < Dockerfile --tag plnet:latest

Run the following command line to train the PLNet on the Wireframe dataset.

python -m hawp.fsl.train configs/plnet.yaml --logdir outputs

We provide a pre-trained model that can be downloaded via OneDrive.

• Test using the Wireframe dataset:

  python -m hawp.fsl.benchmark configs/plnet.yaml \
    --ckpt /path/to/your/model \
    --dataset wireframe
  

• Test using the YorkUrban dataset:

  python -m hawp.fsl.benchmark configs/plnet.yaml \
    --ckpt /path/to/your/model \
    --dataset york
  

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

@article{xu2024airslam,
  title = {{AirSLAM}: An Efficient and Illumination-Robust Point-Line Visual SLAM System},
  author = {Xu, Kuan and Hao, Yuefan and Yuan, Shenghai and Wang, Chen and Xie, Lihua},
  journal = {arXiv preprint arXiv:2408.03520},
  year = {2024},
  url = {https://arxiv.org/abs/2408.03520},
  code = {https://github.com/sair-lab/AirSLAM},
}

This code builds on HAWP and SuperPoint. Please consider citing:

@article{HAWP-journal,
  title = "Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning",
  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
  journal = "IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI)",
  year = {2023}
}

@inproceedings{detone2018superpoint,
  title={Superpoint: Self-supervised interest point detection and description},
  author={DeTone, Daniel and Malisiewicz, Tomasz and Rabinovich, Andrew},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
  pages={224--236},
  year={2018}
}