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OASIS: A Large-Scale Dataset for Single Image 3D in the Wild

Author(s): Chen, Weifang; Qian, Shengyi; Fan, David; Kojima, Noriyuki; Hamilton, Max; et al

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dc.contributor.authorChen, Weifang-
dc.contributor.authorQian, Shengyi-
dc.contributor.authorFan, David-
dc.contributor.authorKojima, Noriyuki-
dc.contributor.authorHamilton, Max-
dc.contributor.authorDeng, Jia-
dc.date.accessioned2021-10-08T19:45:51Z-
dc.date.available2021-10-08T19:45:51Z-
dc.date.issued2020en_US
dc.identifier.citationChen, Weifeng, Shengyi Qian, David Fan, Noriyuki Kojima, Max Hamilton, and Jia Deng. "OASIS: A Large-Scale Dataset for Single Image 3D in the Wild." IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020): pp. 676-685. doi:10.1109/CVPR42600.2020.00076en_US
dc.identifier.issn1063-6919-
dc.identifier.urihttps://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_OASIS_A_Large-Scale_Dataset_for_Single_Image_3D_in_the_CVPR_2020_paper.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1nz4j-
dc.description.abstractSingle-view 3D is the task of recovering 3D properties such as depth and surface normals from a single image. We hypothesize that a major obstacle to single-image 3D is data. We address this issue by presenting Open Annotations of Single Image Surfaces (OASIS), a dataset for single-image 3D in the wild consisting of annotations of detailed 3D geometry for 140,000 images. We train and evaluate leading models on a variety of single-image 3D tasks. We expect OASIS to be a useful resource for 3D vision research. Project site: https://pvl.cs.princeton.edu/OASIS.en_US
dc.format.extent676 - 685en_US
dc.language.isoen_USen_US
dc.relation.ispartofIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)en_US
dc.rightsAuthor's manuscripten_US
dc.titleOASIS: A Large-Scale Dataset for Single Image 3D in the Wilden_US
dc.typeConference Articleen_US
dc.identifier.doi10.1109/CVPR42600.2020.00076-
dc.identifier.eissn2575-7075-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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