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.

翻译：神经辐射场在模拟三维场景外观方面已取得卓越性能。然而，现有方法在处理光泽表面的视角依赖外观时仍面临挑战，尤其是在室内环境复杂光照条件下。与通常假设环境贴图等远场光照的现有方法不同，我们提出一种可学习的高斯方向编码，以更好地建模近场光照条件下的视角依赖效应。重要的是，这种新型方向编码能够捕获近场光照的空间变化特性，并模拟预滤波环境贴图的行为。因此，它能够高效评估任意三维位置在可变粗糙度系数下的预卷积镜面颜色。我们进一步引入了一种数据驱动的几何先验，有助于缓解反射建模中的形状-辐射模糊性问题。实验表明，所提高斯方向编码与几何先验显著改善了神经辐射场中具有挑战性的镜面反射建模，从而有助于将外观分解为更具物理意义的组成部分。