[
    {
        "Variant name": "main",
        "Reviewer name": "Julie Digne <julie.digne@liris.cnrs.fr>",
        "Is master variant (boolean)": true,
        "Is variant deprecated (boolean)": false,
        "Operating system for the test": "Ubuntu 20.04",
        "Title": "PlanIT: planning and instantiating indoor scenes with relation graph and spatial prior networks ",
        "DOI": "10.1145/3306346.3322941",
        "Year": 2019,
        "ACM Keywords": [
            "Probabilistic reasoning",
            "Computer graphics",
            "Neural networks"
        ],
        "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 on Arxiv or any openarchive initiatives (boolean)": false,
        "Arxiv/OAI page URL": "",
        "PDF URL": "https://drive.google.com/file/d/1CJCM6EQyeUWwxdk6tl8cVxEIhV7s3DoA/view",
        "Project URL": "",
        "Code available (boolean)": true,
        "Code URL": "https://github.com/brownvc/planit",
        "Code URL2": "",
        "MD5 sum (for archives)": "",
        "MD5 sum (for archives) URL2": "",
        "git/hg/svn commit hash or revision number": "b42bfda1f7cd7c0e07874728ae7b809c09a3e6b7",
        "git/hg/svn commit hash or revision number URL2": "",
        "Software Heritage permalink": "https://archive.softwareheritage.org/swh:1:dir:5beb70169b7a0396c8e4bfb339d4994f6350120b;origin=https://github.com/brownvc/planit;visit=swh:1:snp:a3c298ae1199d97b23b00949037c588aef43d5cd;anchor=swh:1:rev:b42bfda1f7cd7c0e07874728ae7b809c09a3e6b7;path=//",
        "If code not available, pseudo-code available (boolean)": false,
        "If pseudo-code, could the paper be trivially implemented? {0..4}": 0,
        "Software type {Code, Binary, Partial Code}": "Code",
        "Software language": "python",
        "Code License (if any)": "All rights reserved to Brown",
        "Are the code authors explicit? (boolean)": false,
        "Build/Configure mechanism": "NA",
        "Dependencies": "Not listed",
        "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...)": "pretrained model or training dataset",
        "License of the data": "Data not provided.",
        "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}": 0,
        "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}": 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)": 1,
        "Build instructions/comments": "Could not run due to missing data: the dataset is no longer available, and no pretrained model is given. The necessary packages are not listed in the readme file.",
        "Misc. comments": ""
    }
]