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Pandora is a stereo matching flexible framework made for research and production with state of the art performances:
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Pandora aims at shortening the path between a stereo-matching prototype and its industrialized version.
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By providing a modular pipeline inspired from the (Scharstein et al., 2002) taxonomy, it allows one to emulate, analyse and hopefully improve state of the art stereo algorithms with a few lines of code.
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We (CNES) have actually been using Pandora to create the stereo matching pipeline for the CNES & Airbus <ahref="https://co3d.cnes.fr/en/co3d-0"><imgsrc="https://raw.githubusercontent.com/CNES/Pandora/master/docs/source/Images/logo_co3D_cnes.jpg"width="32"height="32"/></a> off board processing chain.
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Leaning on Pandora's versatility and a fast-paced constantly evolving field we are still calling this framework a work in progress !
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- Inspired from the (Scharstein et al., 2002) modular taxonomy, it allows one to emulate, analyse and hopefully improve state of the art stereo algorithms with a few lines of code.
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- For production purpose, Pandora have been created for the CNES & Airbus <ahref="https://co3d.cnes.fr/en/co3d-0">CO3D project</a> processing chain, as [CARS](https://github.com/CNES/CARS) core stereo matching tool.
#Left (respectively right) disparity map is saved in output_dir/left_disparity.tif (respectively output_dir/right_disparity.tif)
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#Left and right disparity maps are saved in output_dir: left_disparity.tif and right_disparity.tif
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```
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## To go further
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## Documentation
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To create you own stereo matching pipeline and choose among the variety of algorithms we provide, please consult [our online documentation](https://pandora.readthedocs.io/en/stable/index.html).
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You will learn:
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- which stereo matching steps you can [use and combine](https://pandora.readthedocs.io/en/stable/userguide/step_by_step.html)
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- how to quickly set up a [Pandora pipeline](https://pandora.readthedocs.io/en/stable/userguide/sequencing.html)
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- how to add your own private algorithms to [customize your Pandora Framework](https://pandora.readthedocs.io/en/stable/developer_guide/your_plugin.html)
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- how to use [Pandora API](https://pandora.readthedocs.io/en/stable/userguide/as_an_api.html) (see [CARS](https://github.com/CNES/CARS) for real life example)
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To go further, please consult [our online documentation](https://pandora.readthedocs.io/).
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## Credits
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Our data test sample is based on the 2003 Middleburry dataset (D. Scharstein & R. Szeliski, 2003).
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*(D. Scharstein & R. Szeliski, 2002). Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International journal of computer vision, 47(1-3), 7-42.*
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*(D. Scharstein & R. Szeliski, 2003). Scharstein, D., & Szeliski, R. (2003, June). High-accuracy stereo depth maps using structured light. In 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. (Vol. 1, pp. I-I). IEEE.*
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-*Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International journal of computer vision, 47(1-3), 7-42.*
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-*Scharstein, D., & Szeliski, R. (2003, June). High-accuracy stereo depth maps using structured light. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. (Vol. 1, pp. I-I).*
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-*2003 Middleburry dataset (D. Scharstein & R. Szeliski, 2003).*
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## Related
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[Plugin_LibSGM](https://github.com/CNES/pandora_plugin_libsgm) - Stereo Matching Algorithm plugin for Pandora
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[Plugin_MC-CNN](https://github.com/CNES/pandora_plugin_mccnn) - MC-CNN Neural Network plugin for Pandora
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[Pandora2D](https://github.com/CNES/Pandora2D) - CNES Image Registration framework based on Pandora, with 2D disparity maps.
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[CARS](https://github.com/CNES/CARS) - CNES 3D reconstruction software
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## References
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Please cite the following paper when using Pandora:
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*Cournet, M., Sarrazin, E., Dumas, L., Michel, J., Guinet, J., Youssefi, D., Defonte, V., Fardet, Q., 2020. Ground-truth generation and disparity estimation for optical satellite imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.*
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Please cite the following papers when using Pandora:
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-*Cournet, M., Sarrazin, E., Dumas, L., Michel, J., Guinet, J., Youssefi, D., Defonte, V., Fardet, Q., 2020. Ground-truth generation and disparity estimation for optical satellite imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.*
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-*Youssefi D., Michel, J., Sarrazin, E., Buffe, F., Cournet, M., Delvit, J., L’Helguen, C., Melet, O., Emilien, A., Bosman, J., 2020. Cars: A photogrammetry pipeline using dask graphs to construct a global 3d model. IGARSS - IEEE International Geoscience and Remote Sensing Symposium.*
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