Scene 3-D Reconstruction System in Scattering Medium

Research on neural radiance fields for novel view synthesis has experienced explosive growth with the development of new models and extensions. The NeRF (Neural Radiance Fields) algorithm, suitable for underwater scenes or scattering media, is also evolving. Existing underwater 3D reconstruction systems still face challenges such as long training times and low rendering efficiency. This paper proposes an improved underwater 3D reconstruction system to achieve rapid and high-quality 3D reconstruction. First, we enhance underwater videos captured by a monocular camera to correct the image quality degradation caused by the physical properties of the water medium and ensure consistency in enhancement across frames. Then, we perform keyframe selection to optimize resource usage and reduce the impact of dynamic objects on the reconstruction results. After pose estimation using COLMAP, the selected keyframes undergo 3D reconstruction using neural radiance fields (NeRF) based on multiresolution hash encoding for model construction and rendering. In terms of image enhancement, our method has been optimized in certain scenarios, demonstrating effectiveness in image enhancement and better continuity between consecutive frames of the same data. In terms of 3D reconstruction, our method achieved a peak signalto-noise ratio (PSNR) of 18.40 dB and a structural similarity (SSIM) of 0.6677, indicating a good balance between operational efficiency and reconstruction quality.