Detail-preserving depth estimation from a single image based on modified fully convolutional residual network and gradient network
Article Highlights We changed the classic network and loss function to obtain the global 3D depth information of the scene. A depth gradient acquisition scheme is designed to generate the local details of the scene. We can obtain a plausible depth map with better depth details through our developed...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Springer
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/f45e86bba2b9400f8c4fb691f02003f9 |
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| Sumario: | Article Highlights We changed the classic network and loss function to obtain the global 3D depth information of the scene. A depth gradient acquisition scheme is designed to generate the local details of the scene. We can obtain a plausible depth map with better depth details through our developed depth and depth gradients fusion strategy. |
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