Metadata-Version: 1.2
Name: raynet
Version: 0.2
Summary: RayNet implements an end to end trainable 3D reconstruction system
Home-page: http://raynet-mvs.com/
Maintainer: Despoina Paschalidou
Maintainer-email: despoina.paschalidou@tue.mpg.de
License: MIT
Description: RayNet
        ======
        
        This python pachage provides the code that accompanies our CVPR 2018 paper with
        title **RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials**.
        
        Dependencies & Installation
        ---------------------------
        
        Normally, a ``pip install raynet`` should suffice to use our code.
        
        If you already have a functional Keras installation there are not much left to
        install :-)
        
        * ``Keras`` > 2
        * ``Tensorflow``
        * ``Cython``
        * ``PyCuda``
        * ``backports.functools_lru_cache``
        * ``imageio``
        * ``googleapiclient``
        * ``numpy``
        * ``matplotlib``
        
        Depending on how you want to use our code, there are two alternatives regarding
        installation. You can either use a package manager or download and install the
        library manually. For those who just want to use the library we recommend to
        directly install the latest version from *PyPI*, whereas for those who want to
        be able to edit the code we recommend to install the library manually.
        
        
        * *Install from Pypi with:*
        
        .. code:: bash
        
            pip install --user raynet
        
        * *Install manually:*
        
        Clone the `latest version <https://github.com/paschalidoud/raynet>`__ of the library and run
        
        .. code:: bash
        
            # Clone the repository
            git clone git@github.com:paschalidoud/raynet.git
            cd raynet
            # Local installation in development mode
            pip install --user -e .
        
        Documentation
        -------------
        
        The dedicated documentation page can be found in our `documentation site <http://raynet-mvs.com>`__ but you can also read the
        `source code <https://github.com/paschalidoud/raynet>`__  to get an
        idea of how our code can be used. If you have any question regarding the code
        please contact `Despoina Paschalidou <https://avg.is.tuebingen.mpg.de/person/dpaschalidou>`__.
        
        Contribution
        ------------
        
        Contributions such as bug fixes, bug reports, suggestions etc. are more than
        welcome and should be submitted in the form of new issues and/or pull requests
        on Github.
        
        Relevant Research
        -----------------
        
        Below we list some papers that are relevant to the provided code.
        
        **Ours**
        
        * 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>`__]
        
        **By Others**
        
        * Towards Probabilistic Volumetric Reconstruction using Ray Potentials [`pdf <http://www.cvlibs.net/publications/Ulusoy2015THREEDV.pdf>`__]
        * Patches, Planes and Probabilities: A Non-local Prior for Volumetric 3D Reconstruction [`pdf <http://www.cvlibs.net/publications/Ulusoy2016CVPR.pdf>`__]
        * Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels [`pdf <http://www.cvlibs.net/publications/Ulusoy2017CVPR.pdf>`__]
        * Learned Multi Patch Similarity [`pdf <https://arxiv.org/pdf/1703.08836.pdf>`__]
        * SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis [`pdf <https://arxiv.org/pdf/1708.01749.pdf>`__]
        
        Citation
        --------
        If 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::
        
         @InProceedings{Paschalidou_2018_CVPR,
            author = {Paschalidou, Despoina and Ulusoy, Osman and Schmitt, Carolin and Van Gool, Luc and Geiger, Andreas},
            title = {RayNet: Learning Volumetric 3D Reconstruction With Ray Potentials},
            booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
            month = {June},
            year = {2018}
            }
        
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
