@inproceedings{b712235f57284213960267f605d2eb09,
title = "Scale Invariant Multi-view Depth Estimation Network with cGAN Refinement",
abstract = "In this paper we propose a deep learning based depth estimation method for monocular RGB sequences. We train a pair of encoder-decoder network to resolve depth information form image pairs and relative camera poses. To solve scale ambiguous of monocular sequences, a conditional generative adversarial network is applied. Experimental results show that the proposed method can overcome the problem of scale ambiguous and therefore is more suitable for a variety of applications.",
keywords = "Conditional generative adversarial network, Deep learning, Multi-view depth estimation",
author = "Yeh, {Chia Hung} and Huang, {Yao Pao} and Chen, {Mei Juan}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 23rd International Computer Symposium, ICS 2018 ; Conference date: 20-12-2018 Through 22-12-2018",
year = "2019",
doi = "10.1007/978-981-13-9190-3_75",
language = "English",
isbn = "9789811391897",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "681--687",
editor = "Chuan-Yu Chang and Chien-Chou Lin and Horng-Horng Lin",
booktitle = "New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers",
}