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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.|
|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|
|Pages:||676 - 685|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)|
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