@InProceedings{PatriciaSuarez2023,
author="Patricia Suarez
and Dario Carpio
and Angel Sappa",
title="A Deep Learning Based Approach for Synthesizing Realistic Depth Maps",
booktitle="22nd International Conference on Image Analysis and Processing",
year="2023",
volume="14234",
pages="369--380",
abstract="This paper presents a novel cycle generative adversarial network (CycleGAN) architecture for synthesizing high-quality depth maps from a given monocular image. The proposed architecture uses multiple loss functions, including cycle consistency, contrastive, identity, and least square losses, to enable the generation of realistic and high-fidelity depth maps. The proposed approach addresses this challenge by synthesizing depth maps from RGB images without requiring paired training data. Comparisons with several state-of-the-art approaches are provided showing the proposed approach overcome other approaches both in terms of quantitative metrics and visual quality.",
optnote="MSIAU",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3968), last updated on Fri, 26 Jan 2024 13:59:19 +0100",
opturl="https://link.springer.com/chapter/10.1007/978-3-031-43153-1_31"
}