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arXiv · 2405.14847

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

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Liwen Wu, Sai Bi, Zexiang Xu, Fujun Luan, Kai Zhang, Iliyan Georgiev, Kalyan Sunkavalli, Ravi Ramamoorthi. 2024-05-23. Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling. https://arxiv.org/abs/2405.14847

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