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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
161
Depth EstimationScanNet
AbsRel19.8
94
Depth EstimationKITTI
AbsRel48.4
92
Monocular Depth EstimationScanNet
AbsRel19.8
64
Depth EstimationDIODE
Delta-1 Accuracy53.4
62
Depth PredictionETH3D
AbsRel29.2
35
Depth PredictionSintel
AbsRel60.2
32
2D Depth EstimationScanNet
AbsRel29.2
26
Surface Normal EstimationDIODE (test)
L1 Error34.3
24
Monocular Depth EstimationNYU
AbsRel21.9
21
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