Symbol as Points: Panoptic Symbol Spotting via Point-based Representation

📋News

• [2023/03/07] 📢Our code and model weight is release.
• [2024/03/01] 📢Our paper is released in Arxiv, and camera ready version is updated.
• [2024/01/16] 🎊SymPoint is accepted by ICLR 2024.

🔧Installation & Dataset

Environment

We recommend users to use conda to install the running environment. The following dependencies are required:

conda create -n spv1 python=3.8 -y
conda activate spv1

pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install gdown mmcv==0.2.14 svgpathtools==1.6.1 munch==2.5.0 tensorboard==2.12.0 tensorboardx==2.5.1 detectron2==0.6
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'

# compile pointops
cd modules/pointops
python setup.py install

Dataset&Preprocess

download dataset from floorplan website, and convert it to json format data for training and testing.

# download dataset
python download_data.py
# preprocess
#train, val, test
python parse_svg.py --split train --data_dir ./dataset/train/train/svg_gt/
python parse_svg.py --split val --data_dir ./dataset/val/val/svg_gt/
python parse_svg.py --split test --data_dir ./dataset/test/test/svg_gt/

🚀Quick Start

#train
bash tools/train_dist.sh
#test
bash tools/test_dist.sh

🔔Note

As the Attention with Connection Module(ACM) and Contrastive Connection Learning scheme (CCL) are limited for performance, therefore, for simplicity, in this implementation, we abandoned ACM and CCL.

📌Citation

If you find our paper and code useful in your research, please consider giving a star and citation.

    @article{liu2024symbol,
  title={Symbol as Points: Panoptic Symbol Spotting via Point-based Representation},
  author={Liu, Wenlong and Yang, Tianyu and Wang, Yuhan and Yu, Qizhi and Zhang, Lei},
  journal={arXiv preprint arXiv:2401.10556},
  year={2024}
}