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Single-Image Depth Perception in the Wild
Weifeng Chen · Zhao Fu · Dawei Yang · Jia Deng

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #173

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.

Author Information

Weifeng Chen (University of Michigan)
Zhao Fu (University of Michigan)
Dawei Yang (University of Michigan)
Jia Deng (University of Michigan)

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