SpecNeRF：用于镜面反射的高斯方向编码

SpecNeRF: Gaussian Directional Encoding for Specular Reflections

December 20, 2023

作者: Li Ma, Vasu Agrawal, Haithem Turki, Changil Kim, Chen Gao, Pedro Sander, Michael Zollhöfer, Christian Richardt

cs.AI

摘要

神经辐射场在建模3D场景外观方面取得了显著的性能。然而，现有方法仍然在处理具有光泽表面的视角相关外观方面存在困难，特别是在室内环境复杂照明下。与通常假设远程照明（如环境贴图）不同，我们提出了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了近场照明的空间变化特性，并模拟了预过滤环境贴图的行为。因此，它能够有效评估具有不同粗糙度系数的任何3D位置处的预卷积镜面颜色。我们进一步引入了数据驱动的几何先验，有助于缓解反射建模中的形状辐射歧义。我们展示了我们的高斯方向编码和几何先验显着改善了神经辐射场中具有挑战性的镜面反射建模，有助于将外观分解为更具物理意义的组件。

English

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.