分离高光反射，提升复杂光照下光泽表面重建精度
SSR-GS: Separating Specular Reflection in Gaussian Splatting for Glossy Surface Reconstruction
- 用预过滤的Mip-Cubemap建模直接高光反射
- 通过IndiASG模块捕捉间接高光反射，提升真实感
- 结合视觉几何先验，有效抑制反光区误差
近年来，3D高斯点阵（3DGS）在新视角合成方面取得显著进展。然而，在复杂光照条件下准确重建光泽表面仍具挑战性，尤其在强镜面反射与多表面互反射场景中。为此，我们提出SSR-GS，一种面向光泽表面重建的镜面反射建模框架。具体而言，引入预过滤的Mip-Cubemap以高效建模直接镜面反射，并提出IndiASG模块以捕获间接镜面反射。此外，设计视觉几何先验（VGP），通过反射评分（RS）构建反射感知的视觉先验，降低反射主导区域的光度损失权重；几何先验基于VGGT，包含逐级衰减的深度监督和变换后的法线约束。在合成与真实世界数据集上的大量实验表明，SSR-GS在光泽表面重建任务中达到当前最优性能。
原文摘要 · Abstract (English)
In recent years, 3D Gaussian splatting (3DGS) has achieved remarkable progress in novel view synthesis. However, accurately reconstructing glossy surfaces under complex illumination remains challenging, particularly in scenes with strong specular reflections and multi-surface interreflections. To address this issue, we propose SSR-GS, a specular reflection modeling framework for glossy surface reconstruction. Specifically, we introduce a prefiltered Mip-Cubemap to model direct specular reflections efficiently, and propose an IndiASG module to capture indirect specular reflections. Furthermore, we design Visual Geometry Priors (VGP) that couple a reflection-aware visual prior via a reflection score (RS) to downweight the photometric loss contribution of reflection-dominated regions, with geometry priors derived from VGGT, including progressively decayed depth supervision and transformed normal constraints. Extensive experiments on both synthetic and real-world datasets demonstrate that SSR-GS achieves state-of-the-art performance in glossy surface reconstruction.
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