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
If you are interested in image stitching, we sincerely recommend you try our latest work -- UDIS++(ICCV2023, with better capability to handle parallax).
The details of the dataset can be found in our paper. (arXiv, IEEE Xplore)
We release our testing results with the proposed dataset together. One can download it at Google Drive or Baidu Cloud(Extraction code: 1234).
You can try the testing set of the proposed dataset with your own algorithm. And our results in the testing set are also provided with the testing set.
- python 3.6
- numpy 1.18.1
- tensorflow 1.13.1
- tensorlayer 1.8.0
Due to the memory limitation of our GPU, we implement this unsupervised solution in two stages:
- Stage 1 (unsupervised coarse image alignment): please refer to ImageAlignment/ImageAlignment.md.
- Stage 2 (unsupervised image reconstruction): please refer to ImageReconstruction/ImageReconstruction.md.
For windows OS users, you have to change '/' to '\\' in 'line 71 of ImageAlignment/Codes/utils.py' and 'line 48 of ImageReconstruction/Codes/utils.py'.
By resizing the input images to different resolutions, we simulation the change of feature quantity to compare ours with other methods in robustness. The results can be available in https://drive.google.com/drive/folders/1URFKTiUxaZ8i6pcHIKhxVTf-LkTNnXpK?usp=sharing.
Note: Since the RANSAC algorithm randomly selects the sample points, and the feature (SIFT) detection is not strictly consistent each time, different tests on the same image may differ. But the overall performance should be close to the results reported in our experiments.
NIE Lang -- nielang@bjtu.edu.cn
@ARTICLE{9472883,
author={Nie, Lang and Lin, Chunyu and Liao, Kang and Liu, Shuaicheng and Zhao, Yao},
journal={IEEE Transactions on Image Processing},
title={Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images},
year={2021},
volume={30},
number={},
pages={6184-6197},
doi={10.1109/TIP.2021.3092828}}
[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 large-baseline deep homographyn,” arXiv preprint arXiv:2012.06194, 2020.
[3] T. Nguyen, S. W. Chen, S. S. Shivakumar, C. J. Taylor, and V. Kumar. Unsupervised deep homography: A fast and robust homography estimation model. IEEE Robotics and Automation Letters, 3(3):2346–2353, 2018.
[4] J. Zhang, C. Wang, S. Liu, L. Jia, N. Ye, J. Wang, J. Zhou, and J. Sun, “Content-aware unsupervised deep homography estimation,” in European Conference on Computer Vision, pp. 653–669, Springer, 2020.