Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
Refereed conference paper presented and published in conference proceedings


Other information
AbstractThis paper addresses the problem of depth estimation from a single still image. Inspired by recent works on multi- scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. Through extensive experimental evaluation we demonstrate the effective- ness of the proposed approach and establish new state of the art results on publicly available datasets.
Acceptance Date21/07/2017
All Author(s) ListDan Xu, Elisa Ricci, Wanli Ouyang, Xiaogang Wang, Nicu Sebe
Name of Conference2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Start Date of Conference21/07/2017
End Date of Conference26/07/2017
Place of ConferenceHonolulu, Hawaii
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition CVPR 2017
Year2017
Pages161 - 169
ISBN978-1-5386-0457-1
LanguagesEnglish-United States

Last updated on 2018-04-05 at 15:11