Single-Image Depth Perception in the Wild

Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng

{wfchen,zhaofu,ydawei,jiadeng}@umich.edu

Figure 1: Example images and annotations. Green points are those annotated as closer in depth.


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.

Publication

Single-Image Depth Perception in the Wild,
Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng
Neural Information Processing Systems (NIPS), 2016.
[paper][supplementary material][BibTex]

Dataset

Download [Annotations][Test Images (9 GB)][Train/val Images (47 GB)]

CODE

Code for training and evaluation. [link]

CONTACT

Please send any questions or comments to Weifeng Chen at wfchen@umich.edu.