系统梳理水下视觉增强与三维重建关键技术与挑战
Visual enhancement and 3D representation for underwater scenes: a review
- 从物理模型出发，分析水下成像特殊性与传统方法失效原因
- 对比非学习与数据驱动方法在水下场景的性能表现
- 面向科研人员，提供水下视觉领域前沿进展与未来方向
水下视觉增强（UVE）与水下三维重建因水下复杂成像条件，在计算机视觉与基于AI的任务中面临重大挑战。尽管已有众多增强算法发展，但涵盖UVE与水下三维重建的系统性综述仍缺位。为推动该领域研究，本文从多角度进行深入综述：首先介绍基础物理模型，揭示制约传统技术的特殊性；随后梳理专为水下场景设计的先进增强与三维重建方法；评估从非学习方法到数据驱动技术（如神经辐射场、3D高斯溅射）在处理水下失真中的有效性；最后在多个基准数据集上开展定量与定性评估，总结当前最优算法表现，并指出未来关键研究方向。
原文摘要 · Abstract (English)
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.
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