ScatterSplatting: Enhanced View Synthesis in Scattering Scenarios via Joint NeRF and Gaussian Splatting

View synthesis in the scattering medium faces challenges due to image degradation caused by medium scattering, and existing methods struggle to effectively address this issue. Although Neural Radiance Fields (NeRFs) excel in novel view synthesis and high-quality 3D reconstruction, they suffer from slow training speeds and poor handling of scenes with scattering. However, 3D Gaussian Splatting (3DGS) naturally exhibits sparsity, allowing for efficient representation of complex opaque geometries and surface details, but it is limited in modeling medium scattering. To address these limitations, we propose a novel method that combines the volumetric rendering strengths of NeRF with the sparse representation capabilities of 3DGS to effectively handle scattering media. By using 3DGS to represent objects and leveraging volumetric rendering for medium modeling, our method can mitigate the effects of medium scattering, rendering clear scenes in scattering media and synthesizing the appearance and depth of distant objects from novel viewpoints. The experimental results demonstrate a significant improvement in the performance of view-synthesis tasks, with an average increase of 3 to 4 dB. The rendering efficiency improved, with the training time reduced to 8 minutes, while preserving fine details.