We present SSD-GS, a physically-based relighting framework built upon 3D Gaussian Splatting (3DGS) that achieves high-quality reconstruction and photorealistic relighting under novel lighting conditions. In physically-based relighting, accurately modeling light-material interactions is essential for faithful appearance reproduction. However, existing 3DGS-based relighting methods adopt coarse shading decompositions, either modeling only diffuse and specular reflections or relying on neural networks to approximate shadows and scattering. This leads to limited fidelity and poor physical interpretability, particularly for anisotropic metals and translucent materials. To address these limitations, SSD-GS decomposes reflectance into four components: diffuse, specular, shadow, and subsurface scattering. We introduce a learnable dipole-based scattering module for subsurface transport, an occlusion-aware shadow formulation that integrates visibility estimates with a refinement network, and an enhanced specular component with an anisotropic Fresnel-based model. Through progressive integration of all components during training, SSD-GS effectively disentangles lighting and material properties, even for unseen illumination conditions, as demonstrated on the challenging OLAT dataset. Experiments demonstrate superior quantitative and perceptual relighting quality compared to prior methods and pave the way for downstream tasks, including controllable light source editing and interactive scene relighting. The source code is available at: https://github.com/irisfreesiri/SSD-GS.

翻译：我们提出SSD-GS，一种基于3D高斯散射（3DGS）的物理可重照明框架，能够在新型光照条件下实现高质量重建与真实感重照明。在基于物理的重照明中，精确建模光与材料的相互作用对于忠实再现外观至关重要。然而，现有基于3DGS的重照明方法采用粗糙的明暗分解，要么仅建模漫反射和镜面反射，要么依赖神经网络近似阴影和散射。这导致有限保真度和较差的物理可解释性，尤其对于各向异性金属和半透明材料。为克服这些局限，SSD-GS将反射分解为四个分量：漫反射、镜面反射、阴影和次表面散射。我们引入可学习的偶极子散射模块用于次表面传输，提出集成可见性估计与细化网络的遮挡感知阴影公式，并采用各向异性菲涅耳模型增强镜面分量。通过训练过程中所有分量的渐进式整合，SSD-GS有效解耦光照与材料属性，即便对于未见光照条件（如挑战性OLAT数据集所展示）亦如此。实验表明，相比先前方法，本方法在定性和定量重照明质量上均更优，并为可控光源编辑和交互式场景重照明等下游任务奠定基础。源代码见：https://github.com/irisfreesiri/SSD-GS。