Eite-Mono: An Extreme Lightweight Architecture for Self-Supervised Monocular Depth Estimation
In intelligent mine construction, depth prediction via machine vision plays a pivotal role in enhancing visual perception. This need, coupled with the scarcity of high-quality monocular depth estimation datasets, has led to the development of self-supervised learning approaches for depth prediction...
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| Main Author: | |
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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/10606227/ |
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