Visual enhancement and 3D representation for underwater scenes: a review
This review addresses the lack of systematic coverage of both underwater visual enhancement and 3D reconstruction for researchers in computer vision and AI working with aquatic environments.
This paper provides a comprehensive review of underwater visual enhancement and 3D reconstruction methods, surveying approaches from non-learning techniques to advanced data-driven methods like Neural Radiance Fields and 3D Gaussian Splatting, and evaluates state-of-the-art algorithms across multiple benchmark datasets.
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.