NeurIPS 2016poster667 citations
Single-Image Depth Perception in the Wild
Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng
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.
BibTeX
@inproceedings{NIPS2016_0deb1c54,
author = {Chen, Weifeng and Fu, Zhao and Yang, Dawei and Deng, Jia},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Single-Image Depth Perception in the Wild},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/0deb1c54814305ca9ad266f53bc82511-Paper.pdf},
volume = {29},
year = {2016}
}