Visual enhancement and 3D representation for underwater scenes: a review
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912360358215680 |
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| author | Huang, Guoxi Wang, Haoran Seymour, Brett Kovacs, Evan Ellerbrock, John Blackham, Dave Anantrasirichai, Nantheera |
| author_facet | Huang, Guoxi Wang, Haoran Seymour, Brett Kovacs, Evan Ellerbrock, John Blackham, Dave Anantrasirichai, Nantheera |
| contents | Underwater visual enhancement (UVE) and underwater 3D reconstruction pose significant challenges in
computer vision and AI-based tasks due to complex imaging conditions in aquatic environments. Despite
the development of numerous enhancement algorithms, a comprehensive and systematic review covering both
UVE and underwater 3D reconstruction remains absent. To advance research in these areas, we present an
in-depth review from multiple perspectives. First, we introduce the fundamental physical models, highlighting the
peculiarities that challenge conventional techniques. We survey advanced methods for visual enhancement and
3D reconstruction specifically designed for underwater scenarios. The paper assesses various approaches from
non-learning methods to advanced data-driven techniques, including Neural Radiance Fields and 3D Gaussian
Splatting, discussing their effectiveness in handling underwater distortions. Finally, we conduct both quantitative
and qualitative evaluations of state-of-the-art UVE and underwater 3D reconstruction algorithms across multiple
benchmark datasets. Finally, we highlight key research directions for future advancements in underwater vision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01869 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visual enhancement and 3D representation for underwater scenes: a review Huang, Guoxi Wang, Haoran Seymour, Brett Kovacs, Evan Ellerbrock, John Blackham, Dave Anantrasirichai, Nantheera Computer Vision and Pattern Recognition Underwater visual enhancement (UVE) and underwater 3D reconstruction pose significant challenges in computer vision and AI-based tasks due to complex imaging conditions in aquatic environments. Despite the development of numerous enhancement algorithms, a comprehensive and systematic review covering both UVE and underwater 3D reconstruction remains absent. To advance research in these areas, we present an in-depth review from multiple perspectives. First, we introduce the fundamental physical models, highlighting the peculiarities that challenge conventional techniques. We survey advanced methods for visual enhancement and 3D reconstruction specifically designed for underwater scenarios. The paper assesses various approaches from non-learning methods to advanced data-driven techniques, including Neural Radiance Fields and 3D Gaussian Splatting, discussing their effectiveness in handling underwater distortions. Finally, we conduct both quantitative and qualitative evaluations of state-of-the-art UVE and underwater 3D reconstruction algorithms across multiple benchmark datasets. Finally, we highlight key research directions for future advancements in underwater vision. |
| title | Visual enhancement and 3D representation for underwater scenes: a review |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.01869 |