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arXiv · 2007.13215

OASIS: A Large-Scale Dataset for Single Image 3D in the Wild

Weifeng Chen·Shengyi Qian·David Fan·Noriyuki Kojima·Max Hamilton·Jia Deng
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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.

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Weifeng Chen, Shengyi Qian, David Fan, Noriyuki Kojima, Max Hamilton, Jia Deng. 2020-07-26. OASIS: A Large-Scale Dataset for Single Image 3D in the Wild. https://arxiv.org/abs/2007.13215

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