2D Image-Based 3D Scene Retrieval

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dc.contributor.authorAbdul-Rashid, Hameeden_US
dc.contributor.authorYuan, Juefeien_US
dc.contributor.authorLi, Boen_US
dc.contributor.authorLu, Yijuanen_US
dc.contributor.authorBai, Songen_US
dc.contributor.authorBai, Xiangen_US
dc.contributor.authorBui, Ngoc-Minhen_US
dc.contributor.authorDo, Minh N.en_US
dc.contributor.authorDo, Trong-Leen_US
dc.contributor.authorDuong, Anh-Ducen_US
dc.contributor.authorHe, Xinweien_US
dc.contributor.authorLe, Tu-Khiemen_US
dc.contributor.authorLi, Wenhuien_US
dc.contributor.authorLiu, Ananen_US
dc.contributor.authorLiu, Xiaolongen_US
dc.contributor.authorNguyen, Khac-Tuanen_US
dc.contributor.authorNguyen, Vinh-Tiepen_US
dc.contributor.authorNie, Weizhien_US
dc.contributor.authorNinh, Van-Tuen_US
dc.contributor.authorSu, Yutingen_US
dc.contributor.authorTon-That, Vinhen_US
dc.contributor.authorTran, Minh-Trieten_US
dc.contributor.authorXiang, Shuen_US
dc.contributor.authorZhou, Heyuen_US
dc.contributor.authorZhou, Yangen_US
dc.contributor.authorZhou, Zhichaoen_US
dc.contributor.editorTelea, Alex and Theoharis, Theoharis and Veltkamp, Remcoen_US
dc.date.accessioned2018-04-14T18:28:40Z
dc.date.available2018-04-14T18:28:40Z
dc.date.issued2018
dc.description.abstract2D scene image-based 3D scene retrieval is a new research topic in the field of 3D object retrieval. Given a 2D scene image, it is to search for relevant 3D scenes from a dataset. It has an intuitive and convenient framework which allows users to learn, search, and utilize the retrieved results for vast related applications, such as automatic 3D content generation for 3D movie, game and animation production, robotic vision, and consumer electronics apps development, and autonomous vehicles. To advance this promising research, we organize this SHREC track and build the first 2D scene image-based 3D scene retrieval benchmark by collecting 2D images from ImageNet and 3D scenes from Google 3D Warehouse. The benchmark contains uniformly classified 10,000 2D scene images and 1,000 3D scene models of ten (10) categories. In this track, seven (7) groups from five countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only three (3) groups have successfully submitted ten (10) runs of five methods. To have a comprehensive comparison, seven (7) commonly-used retrieval performance metrics have been used to evaluate their retrieval performance. We also suggest several future research directions for this research topic. We wish this publicly available [ARYLL18] benchmark, comparative evaluation results and corresponding evaluation code, will further enrich and boost the research of 2D scene image-based 3D scene retrieval and its applications.en_US
dc.description.sectionheadersSHREC Tracks
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.identifier.doi10.2312/3dor.20181051
dc.identifier.isbn978-3-03868-053-6
dc.identifier.issn1997-0471
dc.identifier.pages37-44
dc.identifier.urihttps://doi.org/10.2312/3dor.20181051
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3dor20181051
dc.publisherThe Eurographics Associationen_US
dc.subjectH.3.3 [Computer Graphics]
dc.subjectInformation Systems
dc.subjectInformation Search and Retrieval
dc.title2D Image-Based 3D Scene Retrievalen_US
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