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

Liwen Wu · Sai Bi · Zexiang Xu · Fujun Luan · Kai Zhang · Iliyan Georgiev · Kalyan Sunkavalli · Ravi Ramamoorthi

Arch 4A-E Poster #156
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Fri 21 Jun 10:30 a.m. PDT — noon PDT


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:

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