[
    {
        "Variant name": "main",
        "Reviewer name": "Nicolas Bonneel <nicolas.bonneel@liris.cnrs.fr>",
        "Is master variant (boolean)": true,
        "Is variant deprecated (boolean)": false,
        "Title": "Perception-driven semi-structured boundary vectorization",
        "DOI": "10.1145/3197517.3201312",
        "Year": 2018,
        "ACM Keywords": [
            "Image manipulation"
        ],
        "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)": false,
        "PDF on the authors' webpage / institution (boolean)": true,
        "PDF URL": "http://www.cs.ubc.ca/labs/imager/tr/2018/PerceptionDrivenVectorization/perception-driven-vectorization.pdf",
        "PDF on Arxiv or any openarchive initiatives (boolean)": false,
        "Arxiv/OAI page URL": "",
        "Project URL": "http://www.cs.ubc.ca/labs/imager/tr/2018/PerceptionDrivenVectorization/",
        "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": "http://www.cs.ubc.ca/labs/imager/tr/2018/PerceptionDrivenVectorization/executable.zip",
        "Code URL2": "",
        "MD5 sum (for archives)": "E3323994E24C0C70D3F9E8EDA5EE242C",
        "git/hg/svn commit hash or revision number": "",
        "MD5 sum (for archives) URL2": "",
        "git/hg/svn commit hash or revision number URL2": "",
        "Software Heritage permalink": "",
        "Software type {Code, Binary, Partial Code}": "Binary",
        "Code License (if any)": "custom",
        "Are the code authors explicit? (boolean)": false,
        "Build/Configure mechanism": "None",
        "Dependencies": "",
        "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, Pre-trained models / Hardcoded data / lookup tables /...",
        "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}": 1,
        "Dependencies score {0=NA, 1,2,3,4,5}": 0,
        "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}": 5,
        "Adaptability score to other contexts {0=NA, 1,2,3,4,5}": 1,
        "Time spent for the test (code download to first successful run, [0,10], 10min slots, 100min max)": 2,
        "Operating system for the test": "Windows",
        "Build instructions/comments": "The binary runs nicely. I could not find the examples shown in the paper for the multicolored examples, but some images are quite similar (e.g., a different penguin, a different red car, a different crown...) and produce reasonable results.\nNote that the required numpy is version 1.16.1 and it doesn't work with 1.14.5 -- the python code is just the GUI.",
        "Misc. comments": "",
        "Software language": "Cannot answer"
    }
]