Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling

Abstract

Novel-view synthesis of specular objects like shiny metals or glossy paints remains a significant challenge. Not only the glossy appearance but also global illumination effects including reflections of other objects in the environment are critical components to faithfully reproduce a scene. In this paper we present Neural Directional Encoding (NDE) a view-dependent appearance encoding of neural radiance fields (NeRF) for rendering specular objects. NDE transfers the concept of feature-grid-based spatial encoding to the angular domain significantly improving the ability to model high-frequency angular signals. In contrast to previous methods that use encoding functions with only angular input we additionally cone-trace spatial features to obtain a spatially varying directional encoding which addresses the challenging interreflection effects. Extensive experiments on both synthetic and real datasets show that a NeRF model with NDE (1) outperforms the state of the art on view synthesis of specular objects and (2) works with small networks to allow fast (real-time) inference. The source code is available at: https://github.com/lwwu2/nde