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Computer Science > Computer Vision and Pattern Recognition

arXiv:1604.03901 (cs)
[Submitted on 13 Apr 2016 (v1), last revised 6 Jan 2017 (this version, v2)]

Title:Single-Image Depth Perception in the Wild

Authors:Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng
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Abstract:This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. We introduce a new dataset "Depth in the Wild" consisting of images in the wild annotated with relative depth between pairs of random points. We also propose a new algorithm that learns to estimate metric depth using annotations of relative depth. Compared to the state of the art, our algorithm is simpler and performs better. Experiments show that our algorithm, combined with existing RGB-D data and our new relative depth annotations, significantly improves single-image depth perception in the wild.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:1604.03901 [cs.CV]
  (or arXiv:1604.03901v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1604.03901

Submission history

From: Weifeng Chen [view email]
[v1] Wed, 13 Apr 2016 18:19:35 UTC (4,819 KB)
[v2] Fri, 6 Jan 2017 16:05:35 UTC (9,305 KB)
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