Lightweight Self-Supervised Monocular Depth Estimation Through CNN and Transformer Integration
Self-supervised monocular depth estimation is a promising research area due to its ability to train models without relying on expensive and difficult-to-obtain ground truth depth labels. In this domain, models often employ Convolutional Neural Networks (CNNs) and Transformers for feature extraction....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10749800/ |
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