🤖 AI Summary
Underwater visual enhancement (UVE) and 3D reconstruction face severe challenges due to inherent optical distortions—namely scattering, absorption, and chromatic aberration—while existing literature lacks a systematic survey on their synergistic development. This paper presents the first unified review of UVE and underwater 3D reconstruction, covering classical models, deep learning, Neural Radiance Fields (NeRF), 3D Gaussian Splatting, and physics-guided approaches. We establish a multi-dimensional evaluation framework and conduct quantitative and qualitative comparisons across mainstream benchmarks, revealing performance boundaries and application suitability under diverse degradation conditions. Furthermore, we identify three key future directions: physics-model integration, cross-domain generalization, and real-time robust reconstruction. This work provides both theoretical foundations and practical guidance for advancing underwater visual understanding and geometric reconstruction.
📝 Abstract
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.