Lang Nie*, Chunyu Lin*, Kang Liao*, Shuaicheng Liu`, Yao Zhao*

* Institute of Information Science, Beijing Jiaotong University

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

We use the UDIS-D dataset to train and evaluate our method. Please refer to UDIS for more details about this dataset.

• numpy 1.19.5
• pytorch 1.7.1
• scikit-image 0.15.0
• tensorboard 2.9.0

We implement this work with Ubuntu, 3090Ti, and CUDA11. Refer to environment.yml for more details.

Similar to UDIS, we also implement this solution in two stages:

• Stage 1 (unsupervised warp): please refer to Warp/readme.md.
• Stage 2 (unsupervised composition): please refer to Composition/readme.md.

If you have any questions about this project, please feel free to drop me an email.

NIE Lang -- [email protected]

@inproceedings{nie2023parallax,
  title={Parallax-Tolerant Unsupervised Deep Image Stitching},
  author={Nie, Lang and Lin, Chunyu and Liao, Kang and Liu, Shuaicheng and Zhao, Yao},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={7399--7408},
  year={2023}
}

[1] L. Nie, C. Lin, K. Liao, M. Liu, and Y. Zhao, “A view-free image stitching network based on global homography,” Journal of Visual Communication and Image Representation, p. 102950, 2020.
[2] L. Nie, C. Lin, K. Liao, and Y. Zhao. Learning edge-preserved image stitching from multi-scale deep homography[J]. Neurocomputing, 2022, 491: 533-543.
[3] L. Nie, C. Lin, K. Liao, S. Liu, and Y. Zhao. Unsupervised deep image stitching: Reconstructing stitched features to images[J]. IEEE Transactions on Image Processing, 2021, 30: 6184-6197.
[4] L. Nie, C. Lin, K. Liao, S. Liu, and Y. Zhao. Deep rectangling for image stitching: a learning baseline[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 5740-5748.