SpecNeRF: Gaussian Directional Encoding for Specular Reflections

Li Ma, Vasu Agrawal, Haithem Turki, Changil Kim, Chen Gao, Pedro Sander, Michael Zollhöfer, Christian Richardt; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 21188-21198

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.