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Underwater self-supervised depth estimation
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DOI：10.1016/j.neucom.2022.09.122.png)
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Abstract
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Accurate underwater depth estimation is a cornerstone of reaching autonomous underwater exploration. However, it is incredibly tricky due to the inherent attenuation character and heavy noise. Fortunately, the depth-changing trend and underwater light attenuation are closely correlated, providing powerful clues for underwater depth estimation. Rather than simulating the underwater attenuation through for-mulas, we propose an underwater self-supervised depth estimation neural network in our work. With the guidance of multiple constraints, which are meticulously designed based on the comprehensive analyses of underwater characters, this network can learn the depth-changing trend by itself from attenuation information in underwater monocular videos. Our detailed experiments on underwater datasets prove that the proposed framework can obtain accurate and fine-grained depth maps. We believe the work may provide an economical solution for underwater perception.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Underwater perception
Depth estimation
Self-supervised
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6.5
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6.5W
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