The reflective appearance, especially strong and typically near-field specular reflections, poses a fundamental challenge for accurate surface reconstruction and novel view synthesis. Existing Gaussian splatting methods either fail to model near-field specular reflections or rely on explicit ray tracing at substantial computational cost. We present \textbf{Ref-DGS}, a reflective dual Gaussian splatting framework that addresses this trade-off by decoupling surface reconstruction from specular reflection within an efficient rasterization-based pipeline. Ref-DGS introduces a dual Gaussian scene representation consisting of geometry Gaussians and complementary local reflection Gaussians that capture near-field specular interactions without explicit ray tracing, along with a global environment reflection field for modeling far-field specular reflections. To predict specular radiance, we further propose a lightweight, physically-aware specular adaptive mixing shader that fuses global and local specular features. Experiments demonstrate that Ref-DGS achieves state-of-the-art performance on reflective scenes while training substantially faster than ray-based Gaussian methods.

翻译：反射外观，尤其是强近场镜面反射，对精确的表面重建和新视角合成构成了根本性挑战。现有的高斯溅射方法要么无法建模近场镜面反射，要么依赖计算成本高昂的显式光线追踪。我们提出\textbf{Ref-DGS}，一种反射式双高斯溅射框架，通过解耦表面重建与镜面反射，在高效的基于光栅化的流程中解决了这一权衡问题。Ref-DGS引入了一种双高斯场景表示，包括几何高斯分量和互补的局部反射高斯分量，无需显式光线追踪即可捕获近场镜面交互，同时结合全局环境反射场以建模远场镜面反射。为预测镜面辐射度，我们进一步提出了一种轻量级、物理感知的镜面自适应混合着色器，该着色器融合了全局与局部镜面特征。实验表明，Ref-DGS在反射场景上达到了最先进的性能，同时训练速度显著快于基于光线的高斯方法。