  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.
