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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Abstract: | Single-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. |
Publication Date: | 2020 |
Citation: | Chen, 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/CVPR42600.2020.00076 |
DOI: | 10.1109/CVPR42600.2020.00076 |
ISSN: | 1063-6919 |
EISSN: | 2575-7075 |
Pages: | 676 - 685 |
Type of Material: | Conference Article |
Journal/Proceeding Title: | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Version: | Author's manuscript |
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