Single Image 3D Without a Single 3D Image
Abstract
Do we really need 3D labels in order to learn how to predict 3D? In this paper, we show that one can learn a mapping from appearance to 3D properties without ever seeing a single explicit 3D label. Rather than use explicit supervision, we use the regularity of indoor scenes to learn the mapping in a completely unsupervised manner. We demonstrate this on both a standard 3D scene understanding dataset as well as Internet images for which 3D is unavailable, precluding supervised learning. Despite never seeing a 3D label, our method produces competitive results.
Cite
Text
Fouhey et al. "Single Image 3D Without a Single 3D Image." International Conference on Computer Vision, 2015. doi:10.1109/ICCV.2015.126Markdown
[Fouhey et al. "Single Image 3D Without a Single 3D Image." International Conference on Computer Vision, 2015.](https://mlanthology.org/iccv/2015/fouhey2015iccv-single/) doi:10.1109/ICCV.2015.126BibTeX
@inproceedings{fouhey2015iccv-single,
title = {{Single Image 3D Without a Single 3D Image}},
author = {Fouhey, David F. and Hussain, Wajahat and Gupta, Abhinav and Hebert, Martial},
booktitle = {International Conference on Computer Vision},
year = {2015},
doi = {10.1109/ICCV.2015.126},
url = {https://mlanthology.org/iccv/2015/fouhey2015iccv-single/}
}