Novel view synthesis (NVS) through non-planar refractive surfaces presents fundamental challenges due to severe, spatially varying optical distortions. While recent representations like NeRF and 3D Gaussian Splatting (3DGS) excel at NVS, their assumption of straight-line ray propagation fails under these conditions, leading to significant artifacts. To overcome this limitation, we introduce RefracGS, a framework that jointly reconstructs the refractive water surface and the scene beneath the interface. Our key insight is to explicitly decouple the refractive boundary from the target objects: the refractive surface is modeled via a neural height field, capturing wave geometry, while the underlying scene is represented as a 3D Gaussian field. We formulate a refraction-aware Gaussian ray tracing approach that accurately computes non-linear ray trajectories using Snell's law and efficiently renders the underlying Gaussian field while backpropagating the loss gradients to the parameterized refractive surface. Through end-to-end joint optimization of both representations, our method ensures high-fidelity NVS and view-consistent surface recovery. Experiments on both synthetic and real-world scenes with complex waves demonstrate that RefracGS outperforms prior refractive methods in visual quality, while achieving 15x faster training and real-time rendering at 200 FPS. The project page for RefracGS is available at https://yimgshao.github.io/refracgs/.

翻译：通过非平面折射表面进行新视角合成（NVS）面临根本性挑战，原因在于严重且空间变化的光学畸变。尽管NeRF和3D高斯泼溅（3DGS）等最新表征方法在NVS中表现优异，但它们假设光线沿直线传播，在折射条件下会产生显著伪影。为克服这一局限，我们提出RefracGS框架，该框架联合重构折射水面及界面下方的场景。其核心思想是将折射边界与目标物体显式解耦：折射表面通过神经高度场建模，捕捉波浪几何形态；而水下场景则表示为3D高斯场。我们设计了一种折射感知的高斯光线追踪方法，利用斯涅尔定律精确计算非线性光线轨迹，高效渲染底层高斯场，同时将损失梯度反向传播至参数化折射表面。通过对两种表征的端到端联合优化，我们的方法实现了高保真NHS和视角一致的表面重建。在复杂波浪的合成场景与真实场景上的实验表明，RefracGS在视觉质量上优于先前的折射方法，同时训练速度提升15倍，并能以200 FPS进行实时渲染。RefracGS项目页面详见https://yimgshao.github.io/refracgs/。