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.

翻译：水下视觉增强（UVE）与水下三维重建因水环境中复杂的成像条件，成为计算机视觉及基于人工智能任务中的重大挑战。尽管已有大量增强算法被提出，但目前仍缺乏同时涵盖UVE与水下三维重建的系统性综述。为推进该领域研究，我们从多角度展开深度综述。首先介绍基础物理模型，着重阐释导致传统技术失效的特殊性；继而系统梳理专为水下场景设计的视觉增强与三维重建先进方法，涵盖从非学习方法到基于Neural Radiance Fields（神经辐射场）和3D Gaussian Splatting（三维高斯溅射）等先进数据驱动技术的各类方案，并探讨其处理水下畸变的效能。最后，我们通过定性与定量评估，在多个基准数据集上对比当前最先进的UVE与水下三维重建算法，并展望水下视觉领域的未来关键研究方向。