Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However, existing approaches still struggle with the view-dependent appearance of glossy surfaces, especially under complex lighting of indoor environments. Unlike existing methods, which typically assume distant lighting like an environment map, we propose a learnable Gaussian directional encoding to better model the view-dependent effects under near-field lighting conditions. Importantly, our new directional encoding captures the spatially-varying nature of near-field lighting and emulates the behavior of prefiltered environment maps. As a result, it enables the efficient evaluation of preconvolved specular color at any 3D location with varying roughness coefficients. We further introduce a data-driven geometry prior that helps alleviate the shape radiance ambiguity in reflection modeling. We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields, which helps decompose appearance into more physically meaningful components.
神经辐射场在模拟3D场景的外观方面取得了显著成绩。然而，现有方法在处理光泽表面的视角依赖外观时仍存在困难，特别是在复杂的室内环境光照下。与通常假设远场光照如环境图的现有方法不同，我们提出了一种可学习的高斯方向编码，以更好地模拟近场光照条件下的视角依赖效应。重要的是，我们的新方向编码捕捉了近场光照的空间变化特性，并模仿了预过滤环境图的行为。结果是，它使得在任何具有不同粗糙度系数的3D位置高效评估预卷积镜面颜色成为可能。我们进一步引入了一个数据驱动的几何形状先验，有助于减轻反射建模中的形状辐射模糊性。我们展示了我们的高斯方向编码和几何形状先验在神经辐射场中显著改善了具有挑战性的镜面反射建模，有助于将外观分解为更物理意义的组成部分。