PT Unknown
AU Patricia Suarez
   Dario Carpio
   Angel Sappa
TI Depth Map Estimation from a Single 2D Image
BT 17th International Conference on Signal-Image Technology & Internet-Based Systems
PY 2023
BP 347
EP 353
DI 10.1109/SITIS61268.2023.00063
AB This paper presents an innovative architecture based on a Cycle Generative Adversarial Network (CycleGAN) for the synthesis of high-quality depth maps from monocular images. The proposed architecture leverages a diverse set of loss functions, including cycle consistency, contrastive, identity, and least square losses, to facilitate the generation of depth maps that exhibit realism and high fidelity. A notable feature of the approach is its ability to synthesize depth maps from grayscale images without the need for paired training data. Extensive comparisons with different state-of-the-art methods show the superiority of the proposed approach in both quantitative metrics and visual quality. This work addresses the challenge of depth map synthesis and offers significant advancements in the field.
ER