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This is the official implementation of PLNet, which is the feature detector of ourAirSLAM. 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 (includingWireframe dataset andYorkUrban dataset) can be downloaded viaGoogle Drive.Many thanks to authors of these two excellent datasets!
You can also use thegdown to download the data in the terminal by
gdown 134L-u9pgGtnzw0auPv8ykHqMjjZ2claOunzip 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==3.9conda activate plnetpip 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 machinepip 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 dependenciespip 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 aDockerfile. You could build the docker image by running the following command lines.
sudo docker build -< Dockerfile --tag plnet:latestRun the following command line to train the PLNet on the Wireframe dataset.
python -m hawp.fsl.train configs/plnet.yaml --logdir outputsWe provide a pre-trained model that can be downloaded viaOneDrive.
Test using the Wireframe dataset:
python -m hawp.fsl.benchmark configs/plnet.yaml \ --ckpt /path/to/your/model \ --dataset wireframeTest 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}}