SpecNeRF: Gaussian Directional Encoding for Specular Reflections
Authors: name: Li Ma, name: Vasu Agrawal, name: Haithem Turki, name: Changil Kim, name: Chen Gao, name: Pedro Sander, name: Michael Zollhöfer, name: Christian Richardt

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.