In underwater images, most useful features are occluded by water. The extent of the occlusion depends on imaging geometry and can vary even across a sequence of burst images. As a result, 3D reconstruction methods robust on in-air scenes, like Neural Radiance Field methods (NeRFs) or 3D Gaussian Splatting (3DGS), fail on underwater scenes. While a recent underwater adaptation of NeRFs achieved state-of-the-art results, it is impractically slow: reconstruction takes hours and its rendering rate, in frames per second (FPS), is less than 1. Here, we present a new method that takes only a few minutes for reconstruction and renders novel underwater scenes at 140 FPS. Named Gaussian Splashing, our method unifies the strengths and speed of 3DGS with an image formation model for capturing scattering, introducing innovations in the rendering and depth estimation procedures and in the 3DGS loss function. Despite the complexities of underwater adaptation, our method produces images at unparalleled speeds with superior details. Moreover, it reveals distant scene details with far greater clarity than other methods, dramatically improving reconstructed and rendered images. We demonstrate results on existing datasets and a new dataset we have collected.
在水下图像中，大多数有用的特征会被水体遮挡，遮挡程度取决于成像几何结构，并且在一系列连拍图像中可能存在变化。因此，针对空气场景表现稳健的 3D 重建方法（如 Neural Radiance Fields (NeRFs) 或 3D Gaussian Splatting (3DGS)）在水下场景中往往失效。尽管最近的水下 NeRFs 改进方法达到了最先进的结果，但其效率极低：重建耗时数小时，渲染速率（每秒帧数，FPS）不足 1。 为解决上述问题，我们提出了一种新方法 Gaussian Splashing，只需几分钟即可完成重建，并以 140 FPS 的速度渲染新的水下场景。该方法结合了 3DGS 的高效性与图像形成模型对散射现象的建模，针对渲染和深度估计过程以及 3DGS 损失函数进行了创新设计。 尽管水下场景适应存在复杂性，Gaussian Splashing 方法能够以无与伦比的速度生成图像，并提供更优的细节表现。此外，该方法显著增强了对远距离场景细节的还原能力，与其他方法相比，大幅提升了重建和渲染图像的质量。 我们在现有数据集和一个新采集的数据集上验证了该方法的效果，结果显示 Gaussian Splashing 不仅在速度上遥遥领先，还在水下图像重建的清晰度和细节表现方面展现了显著优势。