用物理模型提升反射面重建速度与精度
PolGS++: Physically-Guided Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction
- 引入偏振BRDF模型分离漫反射与镜面成分
- 10分钟训练实现高精度表面法线恢复
- 适合虚拟现实与数字内容生成场景
准确重建反射表面是计算机视觉中的基础挑战，广泛应用于实时虚拟现实与数字内容创作。尽管3D高斯点阵（3DGS）能高效实现新视角渲染且具备显式表示，其在反射表面的几何与法线恢复上仍逊于隐式神经方法。为此，我们提出PolGS++，一种基于物理引导的偏振高斯点阵框架，用于快速反射表面重建。具体地，将偏振双向反射分布函数（pBRDF）模型融入3DGS，显式解耦漫反射与镜面分量，提供物理合理的反射建模和更强的几何线索。此外，提出一种深度引导的可见性掩码获取机制，无需昂贵的光线相交计算，即可实现基于偏振角（AoP）的切空间一致性约束。该物理引导设计显著提升重建质量与效率，仅需约10分钟训练时间。大量合成与真实数据集实验验证了方法的有效性。
原文摘要 · Abstract (English)
Accurate reconstruction of reflective surfaces remains a fundamental challenge in computer vision, with broad applications in real-time virtual reality and digital content creation. Although 3D Gaussian Splatting (3DGS) enables efficient novel-view rendering with explicit representations, its performance on reflective surfaces still lags behind implicit neural methods, especially in recovering fine geometry and surface normals. To address this gap, we propose PolGS++, a physically-guided polarimetric Gaussian Splatting framework for fast reflective surface reconstruction. Specifically, we integrate a polarized BRDF (pBRDF) model into 3DGS to explicitly decouple diffuse and specular components, providing physically grounded reflectance modeling and stronger geometric cues for reflective surface recovery. Furthermore, we introduce a depth-guided visibility mask acquisition mechanism that enables angle-of-polarization (AoP)-based tangent-space consistency constraints in Gaussian Splatting without costly ray-tracing intersections. This physically guided design improves reconstruction quality and efficiency, requiring only about 10 minutes of training. Extensive experiments on both synthetic and real-world datasets validate the effectiveness of our method.
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