Deep Rectangling for Image stitching: A Learning Baseline (paper)

Lang Nie¹, Chunyu Lin^{1 *}, Kang Liao¹, Shuaicheng Liu², Yao Zhao¹

¹Institute of Information Science, Beijing Jiaotong University

²School of Information and Communication Engineering, University of Electronic Science and Technology of China

^{{nielang, cylin, kang_liao, yzhao}@bjtu.edu.cn, [email protected]}

Dataset (DIR-D)

The details of the dataset can be found in our paper.

We release our testing results with the proposed dataset together. One can download it in in Google Drive or Baidu Cloud(Extraction code: 1234).

Requirement

• python 3.6
• numpy 1.18.1
• tensorflow 1.13.1

^{We only test it in Ubuntu OS with RXT 2080Ti.}

For windows system

For windows OS users, you have to change '/' to '\\' in 'line 52 of Codes/utils.py'.

Training

Step 1: Download the pretrained vgg19 model

Download VGG-19. Search imagenet-vgg-verydeep-19 in this page and download imagenet-vgg-verydeep-19.mat.

Step 2: Train the network

Modidy the 'Codes/constant.py' to set the 'TRAIN_FOLDER'/'ITERATIONS'/'GPU'. In our experiment, we set 'ITERATIONS' to 100,000.

cd Codes/
python train.py

Testing

Pretrained model for deep rectangling

Our pretrained rectangling model can be available at Google Drive or Baidu Cloud(Extraction code: 1234). And place the four files to 'Codes/checkpoints/Ptrained_model/' folder.

Testing

Modidy the 'Codes/constant.py'to set the 'TEST_FOLDER'/'GPU'. The path for the checkpoint file can be modified in 'Codes/inference.py'.

cd Codes/
python inference.py

Testing with arbitrary resolution images

Modidy the 'Codes_for_Arbitrary_Resolution/constant.py'to set the 'TEST_FOLDER'/'GPU'. The path for the checkpoint file can be modified in 'Codes_for_Arbitrary_Resolution/inference.py'. Then, put the testing images into the folder 'Codes_for_Arbitrary_Resolution/other_dataset/' (including input and mask) and run:

cd Codes_for_Arbitrary_Resolution/
python inference.py

The rectangling results can be found in Codes_for_Arbitrary_Resolution/rectangling/.

Citation

This paper has been accepted by CVPR2022 as oral presentation. If you have any questions, please feel free to contact me.

NIE Lang -- [email protected]

@article{nie2022deep,
      title={Deep Rectangling for Image Stitching: A Learning Baseline}, 
      author={Lang Nie and Chunyu Lin and Kang Liao and Shuaicheng Liu and Yao Zhao},
      journal={arXiv preprint arXiv:2203.03831},
      year={2022},
}

Reference

Lang Nie, Chunyu Lin, Kang Liao, Shuaicheng Liu, and Yao Zhao. Depth-aware multi-grid deep homography estimation with contextual correlation. IEEE Trans. on Circuits and Systems for Video Technology, 2021.