This repository is our C++ implementation of the ECCV 2016 paper, Natural Image Stitching with the Global Similarity Prior. If you use any code or data from our work, please cite our paper.
If you want to build this project under Ubuntu, please refer to https://github.com/Yannnnnnnnnnnn/NISwGSP Thanks a lot to @Yannnnnnnnnnnn!
If you want to build this project under Windows, please refer to https://github.com/firdauslubis88/NISwGSP Thanks a lot to @firdauslubis88!
Download code and compile.
GCC_C_LANGUAGE_STANDARD = GNU99 [-std=gnu99]
CLANG_CXX_LANGUAGE_STANDARD = GNU++14 [-std=gnu++14]
CLANG_CXX_LIBRARY = libc++ (LLVM C++ standard library with C++11 support)
Download input-42-data.
Move [input-42-data] folder to your working directory.
Run the command:
./exe folder_name_in_[input-42-data]_folder
The results can be found in [0_results] folder under [input-42-data] folder.
Optional:
AutoStitch | Ours | Ours(border) |
---|---|---|
AutoStitch | AANAP | Ours |
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AutoStitch | AANAP |
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Ours(2D) | Ours(3D) |
AANAP | Ours |
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AANAP | Ours(2D) | Ours(3D) |
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AutoStitch | AutoStitch + Ours | Ours |
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You can disable debug mode by adding NDEBUG macro. Otherwise you will see the intermediate which is located in the [1_debugs] folder under [input-42-data]. You can download all intermediate data. The intermediate example:
Border | Mesh |
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Initial Features | After sRANSAC |
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Line Data 1 | Line Data 2 |
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If you want to speed up, MATLAB solver is significantly faster than Eigen.
Yu-Sheng Chen and Yung-Yu Chuang.
Natural Image Stitching with Global Similarity Prior. Proceedings of European Conference on Computer Vision 2016 (ECCV 2016), Part V, pp. 186-201, October 2016, Amsterdam, Netherland.
@INPROCEEDINGS{Chen:2016:NIS,
AUTHOR = {Yu-Sheng Chen and Yung-Yu Chuang},
TITLE = {Natural Image Stitching with the Global Similarity Prior},
YEAR = {2016},
MONTH = {October},
BOOKTITLE = {Proceedings of European Conference on Computer Vision (ECCV 2016)},
PAGES = {V186--201},
LOCATION = {Amsterdam},
}
- Chang, C.H., Sato, Y., Chuang, Y.Y.: Shape-preserving half-projective warps for image stitching. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3254-3261. CVPR'14 (2014)
- Gao, J., Kim, S.J., Brown, M.S.: Constructing image panoramas using dual-homography warping. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. pp. 49-56. CVPR'11 (2011)
- Lin, C., Pankanti, S., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015. pp. 1155-1163 (2015)
- Nomura, Y., Zhang, L., Nayar, S.K.: Scene collages and flexible camera arrays. In: Proceedings of the 18th Eurographics Conference on Rendering Techniques. pp. 127-138. EGSR'07 (2007)
- Zaragoza, J., Chin, T.J., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. pp. 2339-2346. CVPR'13 (2013)
Feel free to contact me if there is any question (Yu-Sheng Chen nothinglo@cmlab.csie.ntu.edu.tw).