{
    "identifier": "ark:\/88434\/mds2-2219",
    "accessLevel": "public",
    "references": [
        "https:\/\/www.nist.gov\/publications\/comparison-3d-shape-retrieval-methods-based-large-scale-benchmark-supporting-multimodal",
        "http:\/\/dx.doi.org\/10.1016\/j.cviu.2014.10.006"
    ],
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Afzal A. Godil"
    },
    "programCode": [
        "006:045"
    ],
    "@type": "dcat:Dataset",
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-2219",
    "description": "Objective:\nThe objective of this track is to evaluate the performance of 3D shape retrieval approaches on a large-sale comprehensive 3D shape database which contains different types of models, such as generic, articulated, CAD and architecture models.\n\nIntroduction:\nWith the increasing number of 3D models created every day and stored in databases, the development of effective and scalable 3D search algorithms has become an important research area. In this contest, the task will be retrieving 3D models similar to a complete 3D model query from a new integrated large-scale comprehensive 3D shape benchmark including various types of models. Owing to the integration of the most important existing benchmarks to date, the newly created benchmark is the most exhaustive to date in terms of the number of semantic query categories covered, as well as the variations of model types. The shape retrieval contest will allow researchers to evaluate results of different 3D shape retrieval approaches when applied on a large scale comprehensive 3D database. \n\nThe benchmark is motivated by a latest large collection of human sketches built by Eitz et al. [1]. To explore how human draw sketches and human sketch recognition, they collected 20,000 human-drawn sketches, categorized into 250 classes, each with 80 sketches. This sketch dataset is exhaustive in terms of the number of object categories. Thus, we believe that a 3D model retrieval benchmark based on their object categorizations will be more comprehensive and appropriate than currently available 3D retrieval benchmarks to more objectively and accurately evaluate the real practical performance of a comprehensive 3D model retrieval algorithm if implemented and used in the real world. \n\nConsidering this, we build a SHREC'14 Large Scale Comprehensive Track Benchmark (SHREC14LSGTB) by collecting relevant models in the major previously proposed 3D object retrieval benchmarks. Our target is to find models for as many as classes of the 250 classes and find as many as models for each class. These previous benchmarks have been compiled with different goals in mind and to date, not been considered in their sum. Our work is the first to integrate them to form a new, larger benchmark corpus for comprehensive 3D shape retrieval. \n\nDataset:\nSHREC'14 Large Scale Comprehensive Retrieval Track Benchmark has 8,987 models, categorized into 171 classes. We adopt a voting scheme to classify models. For each classification, we have at least two votes. If these two votes agree each other, we confirm that the classification is correct, otherwise, we perform a third vote to finalize the classification. All the models are categorized according to the classifications in Eitz et al. [1], based on visual similarity. \n\nEvaluation Method:\nTo have a comprehensive evaluation of the retrieval algorithm, we employ seven commonly adopted performance metrics in 3D model retrieval technique.\n\nPlease cite the papers: \n\n[1] Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono, Martin Burtscher, Qiang Chen, Nihad Karim Chowdhury, Bin Fang, Hongbo Fu, Takahiko Furuya, Haisheng Li, Jianzhuang Liu, Henry Johan, Ryuichi Kosaka, Hitoshi Koyanagi, Ryutarou Ohbuchi, Atsushi Tatsuma, Yajuan Wan, Chaoli Zhang, Changqing Zou. A Comparison of 3D Shape Retrieval Methods Based on a Large-scale Benchmark Supporting Multimodal Queries. Computer Vision and Image Understanding, November 4, 2014. \n\n[2] Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono, Qiang Chen, Nihad Karim Chowdhury, Bin Fang, Takahiko Furuya, Henry Johan, Ryuichi Kosaka, Hitoshi Koyanagi, Ryutarou Ohbuchi, Atsushi Tatsuma. SHREC' 14 Track: Large Scale Comprehensive 3D Shape Retrieval. Eurographics Workshop on 3D Object Retrieval 2014 (3DOR 2014): 131-140, 2014.\n",
    "language": [
        "en"
    ],
    "title": "SHREC'14 Track: Large Scale Comprehensive 3D Shape Retrieval",
    "distribution": [
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2219\/SHREC2014_Generic3D.zip.sha256",
            "mediaType": "text\/plain",
            "title": "SHA256 File for SHREC'14 Track: Large Scale Comprehensive 3D Shape Retrieval"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2219\/SHREC2014_Generic3D.zip",
            "description": "3D Models, Classification file, Images, etc.",
            "mediaType": "application\/zip",
            "title": "SHREC'14 Track: Large Scale Comprehensive 3D Shape Retrieval"
        }
    ],
    "license": "https:\/\/www.nist.gov\/open\/license",
    "bureauCode": [
        "006:55"
    ],
    "modified": "2014-01-01 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "theme": [
        "Information Technology:Data and informatics",
        "Mathematics and Statistics:Image and signal processing"
    ],
    "issued": "2020-04-14",
    "keyword": [
        "3D shape retrieval",
        "Large-scale benchmark",
        "Multimodal queries",
        "Performance evaluation",
        "Query-by-Model",
        "Query-by-Example",
        "Evaluation and measurement science."
    ]
}