[
    {
        "Variant name": "main",
        "Reviewer name": "Nicolas Mellado <nmellado0@gmail.com>",
        "Is master variant (boolean)": true,
        "Is variant deprecated (boolean)": false,
        "Title": "End-to-end Optimization of Optics and Image Processing for Achromatic Extended Depth of Field and Super-resolution Imaging",
        "DOI": "10.1145/3197517.3201333",
        "Year": 2018,
        "ACM Keywords": [
            "Computational photography",
            "Reconstruction"
        ],
        "Topic {Rendering, Animation and Simulation, Geometry, Images, Virtual Reality, Fabrication}": "Images",
        "Co-authors from academia (boolean)": true,
        "Co-authors from industry (boolean)": false,
        "ACM Open Access (boolean)": false,
        "PDF on the authors' webpage / institution (boolean)": true,
        "PDF URL": "https://drive.google.com/file/d/1Xums2qyqSGP_z_24HnYpM9gbRm1_uAzY/view",
        "PDF on Arxiv or any openarchive initiatives (boolean)": false,
        "Arxiv/OAI page URL": "",
        "Project URL": "https://vsitzmann.github.io/deepoptics/",
        "Code available (boolean)": true,
        "If code not available, pseudo-code available (boolean)": false,
        "If pseudo-code, could the paper be trivially implemented? {0..4}": "",
        "Code URL": "https://github.com/vsitzmann/deepoptics",
        "Code URL2": "",
        "MD5 sum (for archives)": "",
        "git/hg/svn commit hash or revision number": "21c244dbadc9fb686d5f51a81c5b08a298b36c98",
        "MD5 sum (for archives) URL2": "",
        "git/hg/svn commit hash or revision number URL2": "",
        "Software Heritage permalink": "https://archive.softwareheritage.org/swh:1:dir:d88bbd1829a757ab83fff94f5b0f8935ffddb1f8;origin=https://github.com/vsitzmann/deepoptics/",
        "Software type {Code, Binary, Partial Code}": "Code",
        "Code License (if any)": "unspecified",
        "Are the code authors explicit? (boolean)": false,
        "Build/Configure mechanism": "Not applicable (python, Matlab..)",
        "Dependencies": "tensorflow-gpu / protobuf / poppy",
        "Does the software require paywall/proprietary software/material (boolean)?": false,
        "Does the code need data (not examples) (boolean)": true,
        "Nature of the data (pretrained model, LUT...)": "Training data",
        "License of the data": "Data is provided \"as is\" and without any express or implied warranties, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose. External provider: Inria.",
        "Able to perform a replicability test (boolean)": true,
        "If not able to perform a test, was it due to missing hardware/software? (boolean)": false,
        "Documentation score {0=NA,1,2,3}": 1,
        "Dependencies score {0=NA, 1,2,3,4,5}": 4,
        "Build/configure score {0=NA, 1,2,3,4,5}": 5,
        "Fixing bugs score (if any) {0=NA, 1,2,3,4,5}": 0,
        "Replicate paper results score {0=NA, 1,2,3,4,5}": 3,
        "Adaptability score to other contexts {0=NA, 1,2,3,4,5}": 3,
        "Time spent for the test (code download to first successful run, [0,10], 10min slots, 100min max)": 5,
        "Operating system for the test": "Linux",
        "Build instructions/comments": "I had to patch the environment.yml file to use up-to-date pip packages.\nI could train the network and see image results using tensorboard, however  it is not clear how this images should be compared with the results shown in the paper.",
        "Misc. comments": "",
        "Software language": "Python"
    }
]