The research on neural radiance fields for new view synthesis has experienced explosive growth with the development of new models and extensions. The NERF algorithm, suitable for underwater scenes or scattering media, is also evolving. Existing underwater 3D reconstruction systems still face challenges such as extensive training time and low rendering efficiency. This paper proposes an improved underwater 3D reconstruction system to address these issues and achieve rapid, high-quality 3D reconstruction.To begin with, we enhance underwater videos captured by a monocular camera to correct the poor image quality caused by the physical properties of the water medium while ensuring consistency in enhancement across adjacent frames. Subsequently, we perform keyframe selection on the video frames to optimize resource utilization and eliminate the impact of dynamic objects on the reconstruction results. The selected keyframes, after pose estimation using COLMAP, undergo a three-dimensional reconstruction improvement process using neural radiance fields based on multi-resolution hash coding for model construction and rendering.

翻译：随着新模型和扩展的发展，神经辐射场在新视角合成领域的研究呈爆炸式增长。适用于水下场景或散射介质的NERF算法也在不断演进。现有水下三维重建系统仍面临训练时间长、渲染效率低等挑战。本文提出一种改进的水下三维重建系统，以解决上述问题并实现快速高质量的三维重建。首先，我们对单目相机拍摄的水下视频进行增强处理，在保证相邻帧增强效果一致性的同时，纠正水介质物理特性导致的图像质量缺陷。随后，对视频帧进行关键帧选取以优化资源利用率，并消除动态物体对重建结果的影响。利用COLMAP完成所选关键帧的位姿估计后，通过基于多分辨率哈希编码的神经辐射场进行模型构建与渲染，实现三维重建的改进流程。