PT Unknown
AU Patricia Suarez
   Dario Carpio
   Angel Sappa
TI A Deep Learning Based Approach for Synthesizing Realistic Depth Maps
BT 22nd International Conference on Image Analysis and Processing
PY 2023
BP 369–380
VL 14234
AB 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.
ER