Neural Directional Encoding for Efficient and Accurate View-Dependent
  Appearance Modeling
Authors: name: Liwen Wu, name: Sai Bi, name: Zexiang Xu, name: Fujun Luan, name: Kai Zhang, name: Iliyan Georgiev, name: Kalyan Sunkavalli, name: Ravi Ramamoorthi

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 project webpage and source code are
available at: https://lwwu2.github.io/nde/.