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.

神经辐射场在建模 3D 场景的外观方面取得了显著的性能，然而，现有方法在建模具有光泽表面的视角相关外观时仍存在困难，特别是在复杂照明的室内环境下。与现有方法不同的是，我们提出了一种可学习的高斯方向编码，以更好地模拟近场照明条件下的视角相关效果。重要的是，我们的新方向编码捕捉到近场照明的空间变化特性，并模拟预过滤环境贴图的行为。因此，它能够在具有不同粗糙度系数的任意 3D 位置有效评估预卷积的高光颜色。我们还引入了一种数据驱动的几何先验，帮助缓解反射建模中形状辐射的歧义。我们展示了我们的高斯方向编码和几何先验显著提高了神经辐射场中具有挑战性的高光反射的建模，从而将外观分解为更有物理含义的组成部分。