[
    {
        "Variant name": "main",
        "Reviewer name": "Julie Digne <julie.digne@liris.cnrs.fr>",
        "Is master variant (boolean)": true,
        "Is variant deprecated (boolean)": false,
        "Title": "Deep image-based relighting from optimal sparse samples",
        "DOI": "10.1145/3197517.3201313",
        "Year": 2018,
        "ACM Keywords": [
            "Rendering"
        ],
        "Topic {Rendering, Animation and Simulation, Geometry, Images, Virtual Reality, Fabrication}": "Images",
        "Co-authors from academia (boolean)": true,
        "Co-authors from industry (boolean)": true,
        "ACM Open Access (boolean)": true,
        "PDF on the authors' webpage / institution (boolean)": true,
        "PDF URL": "https://www.ics.uci.edu/~yug10/projects/megvii/ref/Xu%20-%202018%20-%20Deep%20Image-Based%20Relighting%20from%20Optimal%20Sparse%20Samples.pdf",
        "PDF on Arxiv or any openarchive initiatives (boolean)": false,
        "Arxiv/OAI page URL": "",
        "Project URL": "http://cseweb.ucsd.edu/~viscomp/projects/SIG18Relighting/",
        "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/zexiangxu/Deep-Relighting",
        "Code URL2": "",
        "MD5 sum (for archives)": "",
        "git/hg/svn commit hash or revision number": "f29e08bcd7ac72fee591a4bded1d0a5c8e873d59",
        "MD5 sum (for archives) URL2": "",
        "git/hg/svn commit hash or revision number URL2": "",
        "Software Heritage permalink": "https://archive.softwareheritage.org/swh:1:dir:a0023af5c8770d20b0cbfb1187a50e39be7c90c4;origin=https://github.com/zexiangxu/Deep-Relighting/",
        "Software type {Code, Binary, Partial Code}": "Code",
        "Code License (if any)": "Disclaimer:This software and related data are published for academic and non-commercial use only.",
        "Are the code authors explicit? (boolean)": false,
        "Build/Configure mechanism": "This software and related data are published for academic and non-commercial use only.",
        "Dependencies": "tensorflow",
        "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": "published for academic and non-commercial use only",
        "Able to perform a replicability test (boolean)": false,
        "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}": 5,
        "Build/configure score {0=NA, 1,2,3,4,5}": 0,
        "Fixing bugs score (if any) {0=NA, 1,2,3,4,5}": 0,
        "Replicate paper results score {0=NA, 1,2,3,4,5}": 0,
        "Adaptability score to other contexts {0=NA, 1,2,3,4,5}": 0,
        "Time spent for the test (code download to first successful run, [0,10], 10min slots, 100min max)": 10,
        "Operating system for the test": "Linux",
        "Build instructions/comments": "Unable to unzip the training data (320Go zip file) (64Go RAM computer).",
        "Misc. comments": "",
        "Software language": "Python"
    }
]