PolGS++: 基于物理的极化高斯点散布用于快速反射表面重建
PolGS++: Physically-Guided Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction
AI总结：
PolGS++通过整合极化BRDF模型和深度引导的可见性遮罩机制，实现高效且准确的反射表面重建。
摘要：
准确重建反射表面仍然是计算机视觉中的基本挑战，在实时虚拟现实和数字内容创作中有广泛的应用。尽管3D高斯点散布（3DGS）能够通过显式表示实现高效的新型视角渲染，但其在反射表面上的性能仍落后于隐式神经方法，特别是在恢复精细几何和表面法线方面。为解决这一差距，我们提出了PolGS++，一种基于物理的极化高斯点散布框架，用于快速反射表面重建。具体而言，我们将极化BRDF（pBRDF）模型整合到3DGS中，以显式解耦漫反射和镜面成分，提供基于物理的反射率建模，并为反射表面恢复提供更强的几何线索。此外，我们引入了一种基于深度的可见性遮罩获取机制，该机制能够在高斯点散布中实现基于极化角度（AoP）的切线空间一致性约束，而无需昂贵的光线追踪相交。这种基于物理的设计提高了重建质量和效率，仅需约10分钟的训练时间。在合成和真实世界数据集上的大量实验验证了我们方法的有效性。
英文摘要：
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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