Accurate reconstruction and relighting of glossy objects remain a longstanding challenge, as object shape, material properties, and illumination are inherently difficult to disentangle. Existing neural rendering approaches often rely on simplified BRDF models or parameterizations that couple diffuse and specular components, which restricts faithful material recovery and limits relighting fidelity. We propose a relightable framework that integrates a microfacet BRDF with the specular-glossiness parameterization into 2D Gaussian Splatting with deferred shading. This formulation enables more physically consistent material decomposition, while diffusion-based priors for surface normals and diffuse color guide early-stage optimization and mitigate ambiguity. A coarse-to-fine optimization of the environment map accelerates convergence and preserves high-dynamic-range specular reflections. Extensive experiments on complex, glossy scenes demonstrate that our method achieves high-quality geometry and material reconstruction, delivering substantially more realistic and consistent relighting under novel illumination compared to existing Gaussian splatting methods.

翻译：光泽物体的精确重建与重光照一直是一个长期存在的挑战，因为物体形状、材质属性与光照本质上难以解耦。现有的神经渲染方法通常依赖于简化的BRDF模型或将漫反射与高光分量耦合的参数化方式，这限制了对材质的忠实恢复并降低了重光照的保真度。我们提出了一种可重光照框架，将微表面BRDF与高光-光泽度参数化集成到采用延迟着色的2D高斯泼溅中。该公式实现了更符合物理规律的材质分解，同时基于扩散模型的表面法向与漫反射颜色先验可指导早期优化并缓解歧义性。环境贴图的由粗到精优化加速了收敛并保留了高动态范围的高光反射。在复杂光泽场景上进行的大量实验表明，我们的方法实现了高质量的几何与材质重建，与现有高斯泼溅方法相比，在新颖光照下能提供显著更真实、更一致的重光照效果。