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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

TaskDatasetResultRank
Monocular Depth EstimationKITTI
Abs Rel0.317
220
Depth EstimationKITTI--
156
Depth EstimationScanNet
AbsRel19.8
121
Monocular Depth EstimationScanNet
AbsRel19.8
103
Depth EstimationDIODE
Delta-1 Accuracy53.4
82
Surface Normal EstimationNYU V2
Mean Angular Error29.2
65
Depth PredictionETH3D
AbsRel29.2
37
Surface Normal EstimationiBIMS-1
MAE32.6
34
Depth PredictionSintel
AbsRel60.2
32
Monocular Depth EstimationNYU
AbsRel21.9
26
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