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Author  |
Patricia Suarez; Dario Carpio; Angel Sappa | ||||
| Title | A Deep Learning Based Approach for Synthesizing Realistic Depth Maps | Type | Conference Article | ||
| Year | 2023 | Publication | 22nd International Conference on Image Analysis and Processing | Abbreviated Journal | |
| Volume | 14234 | Issue | Pages | 369–380 | |
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| 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. | ||||
| Address | Udine; Italia; Setember 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ICIAP | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SCS2023 | Serial | 3968 | ||
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