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AMENet is a monocular depth estimation network designed for automatic stereoscopic display
- Source :
- Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
- Publication Year :
- 2024
- Publisher :
- Nature Portfolio, 2024.
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Abstract
- Abstract Monocular depth estimation has a wide range of applications in the field of autostereoscopic displays, while accuracy and robustness in complex scenes are still a challenge. In this paper, we propose a depth estimation network for autostereoscopic displays, which aims at improving the accuracy of monocular depth estimation by fusing Vision Transformer (ViT) and Convolutional Neural Network (CNN). Our approach feeds the input image as a sequence of visual features into the ViT module and utilizes its global perception capability to extract high-level semantic features of the image. The relationship between the losses is quantified by adding a weight correction module to improve robustness of the model. Experimental evaluation results on several public datasets show that AMENet exhibits higher accuracy and robustness than existing methods in different scenarios and complex conditions. In addition, a detailed experimental analysis was conducted to verify the effectiveness and stability of our method. The accuracy improvement on the KITTI dataset compared to the baseline method is 4.4%. In summary, AMENet is a promising depth estimation method with sufficient high robustness and accuracy for monocular depth estimation tasks.
- Subjects :
- Depth loss
Monocular depth estimation
CNN
Transformer
Medicine
Science
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 14
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.b93ba906a32a49a2b3464c1fa215b95c
- Document Type :
- article
- Full Text :
- https://doi.org/10.1038/s41598-024-56095-1