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    "info": {
        "author": "Despoina Paschalidou",
        "author_email": "despoina.paschalidou@tue.mpg.de",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering"
        ],
        "description": "RayNet\n======\n\nThis python pachage provides the code that accompanies our CVPR 2018 paper with\ntitle **RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials**.\n\nDependencies & Installation\n---------------------------\n\nNormally, a ``pip install raynet`` should suffice to use our code.\n\nIf you already have a functional Keras installation there are not much left to\ninstall :-)\n\n* ``Keras`` > 2\n* ``Tensorflow``\n* ``Cython``\n* ``PyCuda``\n* ``backports.functools_lru_cache``\n* ``imageio``\n* ``googleapiclient``\n* ``numpy``\n* ``matplotlib``\n\nDepending on how you want to use our code, there are two alternatives regarding\ninstallation. You can either use a package manager or download and install the\nlibrary manually. For those who just want to use the library we recommend to\ndirectly install the latest version from *PyPI*, whereas for those who want to\nbe able to edit the code we recommend to install the library manually.\n\n\n* *Install from Pypi with:*\n\n.. code:: bash\n\n    pip install --user raynet\n\n* *Install manually:*\n\nClone the `latest version <https://github.com/paschalidoud/raynet>`__ of the library and run\n\n.. code:: bash\n\n    # Clone the repository\n    git clone git@github.com:paschalidoud/raynet.git\n    cd raynet\n    # Local installation in development mode\n    pip install --user -e .\n\nDocumentation\n-------------\n\nThe dedicated documentation page can be found in our `documentation site <http://raynet-mvs.com>`__ but you can also read the\n`source code <https://github.com/paschalidoud/raynet>`__  to get an\nidea of how our code can be used. If you have any question regarding the code\nplease contact `Despoina Paschalidou <https://avg.is.tuebingen.mpg.de/person/dpaschalidou>`__.\n\nContribution\n------------\n\nContributions such as bug fixes, bug reports, suggestions etc. are more than\nwelcome and should be submitted in the form of new issues and/or pull requests\non Github.\n\nRelevant Research\n-----------------\n\nBelow we list some papers that are relevant to the provided code.\n\n**Ours**\n\n* RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials [`pdf <http://openaccess.thecvf.com/content_cvpr_2018/papers/Paschalidou_RayNet_Learning_Volumetric_CVPR_2018_paper.pdf>`__]\n\n**By Others**\n\n* Towards Probabilistic Volumetric Reconstruction using Ray Potentials [`pdf <http://www.cvlibs.net/publications/Ulusoy2015THREEDV.pdf>`__]\n* Patches, Planes and Probabilities: A Non-local Prior for Volumetric 3D Reconstruction [`pdf <http://www.cvlibs.net/publications/Ulusoy2016CVPR.pdf>`__]\n* Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels [`pdf <http://www.cvlibs.net/publications/Ulusoy2017CVPR.pdf>`__]\n* Learned Multi Patch Similarity [`pdf <https://arxiv.org/pdf/1703.08836.pdf>`__]\n* SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis [`pdf <https://arxiv.org/pdf/1708.01749.pdf>`__]\n\nCitation\n--------\nIf you are using our code, please cite `our paper <http://openaccess.thecvf.com/content_cvpr_2018/papers/Paschalidou_RayNet_Learning_Volumetric_CVPR_2018_paper.pdf>`__.The BibTeX reference is::\n\n @InProceedings{Paschalidou_2018_CVPR,\n    author = {Paschalidou, Despoina and Ulusoy, Osman and Schmitt, Carolin and Van Gool, Luc and Geiger, Andreas},\n    title = {RayNet: Learning Volumetric 3D Reconstruction With Ray Potentials},\n    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\n    month = {June},\n    year = {2018}\n    }\n\n\n",
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