TY  - CONF
AU  - Patricia Suarez
AU  - Dario Carpio
AU  - Angel Sappa
A2  - ICIAP
PY  - 2023//
TI  - A Deep Learning Based Approach for Synthesizing Realistic Depth Maps
T2  - LNCS
BT  - 22nd International Conference on Image Analysis and Processing
SP  - 369–380
VL  - 14234
N2  - 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.
UR  - https://link.springer.com/chapter/10.1007/978-3-031-43153-1_31
N1  - MSIAU
ID  - Patricia Suarez2023
ER  -