@inproceedings{6e17bb1393ca4416bbabecca77db8614,
title = "Surface Normals in the Wild",
abstract = "We study the problem of single-image depth estimation for images in the wild. We collect human annotated surface normals and use them to help train a neural network that directly predicts pixel-wise depth. We propose two novel loss functions for training with surface normal annotations. Experiments on NYU Depth, KITTI, and our own dataset demonstrate that our approach can significantly improve the quality of depth estimation in the wild.",
author = "Weifeng Chen and Donglai Xiang and Jia Deng",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Computer Vision, ICCV 2017 ; Conference date: 22-10-2017 Through 29-10-2017",
year = "2017",
month = dec,
day = "22",
doi = "10.1109/ICCV.2017.173",
language = "English (US)",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1566--1575",
booktitle = "Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017",
address = "United States",
}