解决高反光场景中形状模糊问题，实现精准法向估计与高质量渲染。
Normal-NeRF: Ambiguity-Robust Normal Estimation for Highly Reflective Scenes
- 基于透射率梯度的法向估计，抗形状歧义。
- 双激活密度模块平衡平滑法向与锐利边界。
- 适合复杂高反光场景重建，提升真实感渲染效果。
神经辐射场（NeRF）在重建和渲染高反光场景时表现不佳。尽管已有反射感知外观模型改善镜面反射渲染，但镜面表面固有的形状歧义仍阻碍了鲁棒重建。现有方法依赖额外几何先验正则化形状预测，易导致复杂场景中几何过度平滑。我们观察到表面法向在参数化反射中的关键作用，提出一种基于透射率梯度的法向估计技术，可在形状模糊条件下保持鲁棒性。此外，设计了双激活密度模块，有效弥合平滑法向与锐利物体边界之间的差距。结合反射感知外观模型，所提方法实现了兼具高度镜面反射与复杂几何结构场景的鲁棒重建与高保真渲染。大量实验表明，该方法在多个数据集上优于现有最先进方法。
原文摘要 · Abstract (English)
Neural Radiance Fields (NeRF) often struggle with reconstructing and rendering highly reflective scenes. Recent advancements have developed various reflection-aware appearance models to enhance NeRF's capability to render specular reflections. However, the robust reconstruction of highly reflective scenes is still hindered by the inherent shape ambiguity on specular surfaces. Existing methods typically rely on additional geometry priors to regularize the shape prediction, but this can lead to oversmoothed geometry in complex scenes. Observing the critical role of surface normals in parameterizing reflections, we introduce a transmittance-gradient-based normal estimation technique that remains robust even under ambiguous shape conditions. Furthermore, we propose a dual activated densities module that effectively bridges the gap between smooth surface normals and sharp object boundaries. Combined with a reflection-aware appearance model, our proposed method achieves robust reconstruction and high-fidelity rendering of scenes featuring both highly specular reflections and intricate geometric structures. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on various datasets.
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