OASIS: A Large-Scale Dataset for Single Image 3D in the Wild
About
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.
Weifeng Chen, Shengyi Qian, David Fan, Noriyuki Kojima, Max Hamilton, Jia Deng• 2020
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Monocular Depth Estimation | KITTI | Abs Rel0.317 | 220 | |
| Depth Estimation | KITTI | -- | 156 | |
| Depth Estimation | ScanNet | AbsRel19.8 | 121 | |
| Monocular Depth Estimation | ScanNet | AbsRel19.8 | 103 | |
| Depth Estimation | DIODE | Delta-1 Accuracy53.4 | 82 | |
| Surface Normal Estimation | NYU V2 | Mean Angular Error29.2 | 65 | |
| Depth Prediction | ETH3D | AbsRel29.2 | 37 | |
| Surface Normal Estimation | iBIMS-1 | MAE32.6 | 34 | |
| Depth Prediction | Sintel | AbsRel60.2 | 32 | |
| Monocular Depth Estimation | NYU | AbsRel21.9 | 26 |
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