  Water caustics are commonly observed in seafloor imaging data from
shallow-water areas. Traditional methods that remove caustic patterns from
images often rely on 2D filtering or pre-training on an annotated dataset,
hindering the performance when generalizing to real-world seafloor data with 3D
structures. In this paper, we present a novel method Recurrent Gaussian
Splatting (RecGS), which takes advantage of today's photorealistic 3D
reconstruction technology, 3DGS, to separate caustics from seafloor imagery.
With a sequence of images taken by an underwater robot, we build 3DGS
recurrently and decompose the caustic with low-pass filtering in each
iteration. In the experiments, we analyze and compare with different methods,
including joint optimization, 2D filtering, and deep learning approaches. The
results show that our method can effectively separate the caustic from the
seafloor, improving the visual appearance, and can be potentially applied on
more problems with inconsistent illumination.
