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, 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 recursively 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.
水面光泽通常在浅水区的海底成像数据中观察到。传统的去除图像中光泽模式的方法通常依赖于2D滤波或在标注数据集上的预训练，这在推广到具有3D结构的实际海底数据时会妨碍性能。在本文中，我们提出了一种新的方法——循环高斯喷溅（Recurrent Gaussian Splatting，简称RecGS），该方法利用当今的逼真3D重建技术3DGS，从海底图像中分离光泽。通过一系列由水下机器人拍摄的图像，我们循环地构建3DGS，并在每次迭代中通过低通滤波分解光泽。在实验中，我们分析并与不同方法进行比较，包括联合优化、2D滤波和深度学习方法。结果表明，我们的方法可以有效地从海底分离光泽，改善视觉外观，并有可能应用于更多具有不一致光照的问题。