[
    {
        "Variant name": "main",
        "Reviewer name": "David Coeurjolly <david.coeurjolly@liris.cnrs.fr>",
        "Is master variant (boolean)": true,
        "Is variant deprecated (boolean)": false,
        "Title": "Predictive and generative neural networks for object functionality",
        "DOI": "10.1145/3197517.3201287",
        "Year": 2018,
        "ACM Keywords": [
            "Shape analysis"
        ],
        "Topic {Rendering, Animation and Simulation, Geometry, Images, Virtual Reality, Fabrication}": "Geometry",
        "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": "http://vcc.tech/file/upload_file//image/research/att201805111718/ICON4.pdf",
        "PDF on Arxiv or any openarchive initiatives (boolean)": false,
        "Arxiv/OAI page URL": "",
        "Project URL": "https://vcc.tech/research/2018/ICON4",
        "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/Salingo/PGDNN",
        "Code URL2": "",
        "MD5 sum (for archives)": "",
        "git/hg/svn commit hash or revision number": "72c801c890ab8233dbbe4a9cb4cfee8a69e7d494",
        "MD5 sum (for archives) URL2": "",
        "git/hg/svn commit hash or revision number URL2": "",
        "Software Heritage permalink": "https://archive.softwareheritage.org/swh:1:dir:3ea5cfc3fbd91d1de1cedd2e20d6b63b6d5568ce;origin=https://github.com/Salingo/PGDNN/",
        "Software type {Code, Binary, Partial Code}": "Code",
        "Code License (if any)": "MIT",
        "Are the code authors explicit? (boolean)": false,
        "Build/Configure mechanism": "Not applicable (python, Matlab..)",
        "Dependencies": "tensorflow / numpy / scipy",
        "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": "",
        "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}": 0,
        "Dependencies score {0=NA, 1,2,3,4,5}": 5,
        "Build/configure score {0=NA, 1,2,3,4,5}": 5,
        "Fixing bugs score (if any) {0=NA, 1,2,3,4,5}": 4,
        "Replicate paper results score {0=NA, 1,2,3,4,5}": 0,
        "Adaptability score to other contexts {0=NA, 1,2,3,4,5}": 4,
        "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": "Missing dependencies : numpy and scipy but easily installable. The code does not provide the pretrained models and requires to train three different networks (15000 epochs (2-3min per 10 epochs on the test machine)). I was not able to check the results and thus evaluate if the results match with the paper ones.",
        "Misc. comments": "",
        "Software language": "Python"
    }
]