Polarization-aware Neural Radiance Fields (NeRF) enable novel view synthesis of specular-reflection scenes but face challenges in slow training, inefficient rendering, and strong dependencies on material/viewpoint assumptions. However, 3D Gaussian Splatting (3DGS) enables real-time rendering yet struggles with accurate reflection reconstruction from reflection-geometry entanglement, adding a deferred reflection module introduces environment map dependence. We address these limitations by proposing PolarGuide-GSDR, a polarization-forward-guided paradigm establishing a bidirectional coupling mechanism between polarization and 3DGS: first 3DGS's geometric priors are leveraged to resolve polarization ambiguity, and then the refined polarization information cues are used to guide 3DGS's normal and spherical harmonic representation. This process achieves high-fidelity reflection separation and full-scene reconstruction without requiring environment maps or restrictive material assumptions. We demonstrate on public and self-collected datasets that PolarGuide-GSDR achieves state-of-the-art performance in specular reconstruction, normal estimation, and novel view synthesis, all while maintaining real-time rendering capabilities. To our knowledge, this is the first framework embedding polarization priors directly into 3DGS optimization, yielding superior interpretability and real-time performance for modeling complex reflective scenes.

翻译：基于偏振感知的神经辐射场（NeRF）能够实现镜面反射场景的新视角合成，但面临训练缓慢、渲染效率低以及对材质/视角假设依赖性强等挑战。然而，三维高斯泼溅（3DGS）虽能实现实时渲染，却因反射与几何纠缠而难以准确重建反射，引入延迟反射模块又带来环境贴图依赖。为克服这些局限，我们提出PolarGuide-GSDR，一种偏振前向引导范式，在偏振与3DGS间建立双向耦合机制：首先利用3DGS的几何先验解决偏振模糊性问题，随后利用精炼的偏振信息线索指导3DGS的法向量与球谐函数表示。该过程无需环境贴图或严格的材质假设，即可实现高保真反射分离与全场景重建。我们在公开及自采集数据集上验证，PolarGuide-GSDR在镜面反射重建、法向量估计和新视角合成方面均达到最先进性能，同时保持实时渲染能力。据我们所知，这是首个将偏振先验直接嵌入3DGS优化的框架，为复杂反射场景建模提供了卓越的可解释性与实时性能。