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 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 adaptive mixing shader that fuses global and local reflection 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，尤其是强且通常为近场的镜面反射，对精确的表面重建和新视角合成构成了根本性挑战。现有的高斯泼溅方法要么无法建模近场镜面反射，要么依赖于显式光线追踪，计算成本高昂。我们提出了Ref-DGS，一个反射式双高斯泼溅框架，它通过在一个高效的基于光栅化的流程中将表面重建与镜面反射解耦，来解决这一权衡问题。Ref-DGS引入了一种双高斯场景表示，由几何高斯和互补的局部反射高斯组成，后者无需显式光线追踪即可捕捉近场镜面交互，同时还包含一个用于建模远场镜面反射的全局环境反射场。为了预测镜面辐射度，我们进一步提出了一种轻量级、物理感知的自适应混合着色器，用于融合全局和局部反射特征。实验表明，Ref-DGS在反射场景上实现了最先进的性能，同时训练速度显著快于基于光线的高斯方法。