Parallax-Tolerant Unsupervised Deep Image Stitching (UDIS++ paper)
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 -- nielang@bjtu.edu.cn
@article{nie2023parallaxtolerant,
title={Parallax-Tolerant Unsupervised Deep Image Stitching},
author={Lang Nie and Chunyu Lin and Kang Liao and Shuaicheng Liu and Yao Zhao},
journal={arXiv preprint arXiv:2302.08207},
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.