Official implementation for "Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching" [paper] [supp] [video] [arxiv].

Source code for baseline method (SLNet) is available in here.

Also, more recent work can be found in here.

• PyTorch 1.3.1
• CUDA 10.0
• CuDNN 7.6.5
• python 3.6

Create conda environment:

    $ conda create -n DRM python=3.6 anaconda
    $ conda activate DRM
    $ pip install opencv-python==3.4.2.16
    $ conda install pytorch==1.3.1 torchvision cudatoolkit=10.0 -c pytorch

Download repository:

    $ git clone https://github.com/dongkwonjin/Semantic-Line-DRM.git

1. Download the following datasets to root/. SEL and SEL_Hard are datasets for semantic line detection. Others are datasets for applications. We obtain the edge detection results in edge folder, by employing HED algorithm.

2. Download our model parameters to root/(task_folder_name)/ if you want to get the performance of the paper.

3. Edit config.py. Please modify dataset_dir and paper_weight_dir. If you want to get the performance of the paper, please input run_mode to 'test_paper'.

4. Run with

cd Semantic-Line-DRM-master/(task_folder_name)/(model_folder_name)/code/
python main.py

@Inproceedings{
    Jin2020DRM,
    title={Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching},
    author={Dongkwon Jin, Jun-Tae Lee, and Chang-Su Kim},
    booktitle={ECCV},
    year={2020}
}