Integrating Semantic Line Detection and Scene Awareness

Implementation of "Integrating Semantic Line Detection and Scene-Awareness" RSJ2024

Data Downloading

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

For annotating the dataset, please refer to the annotation tool available at this link. The full annotated data will released soon.

Installation

Setting Up the Virtual Environment

Clone the repository:

git clone https://github.com/Mulugeta-Solomon/Integrating-Semantic-Line-Detection-and-Scene-Awareness.git
cd Integrating-Semantic-Line-Detection-and-Scene-Awareness

Install ninja-build by:

sudo apt install ninja-build.

Create and activate a virtual environment:

python -m venv torchenv
source torchenv/bin/activate  # On Windows use: venv\Scripts\activate

Install pytorch:

# 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 --upgrade pip
pip install -r requirements.txt

Verify the installation.

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

Citation

If you find this work helpful in your research, please cite it as follows:

@inproceedings{RSJ2024AC1G1-02,
  title     = {Integrating Semantic Line Detection and Scene-Awareness},
  author    = {Mulugeta Solomon Abate and Tian Yang and Kazuhiro Shimonomura},
  booktitle = {Proceedings of RSJ2024},
  year      = {2024}
}

Acknowledgement

This project is built on hawp. We acknowledge Dolphin Mulugeta for his contribution to the annotation tool. We also acknowledge the author of the wireframe dataset.