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

• URL: http://arxiv.org/abs/2405.14847v1
• Date: Thu, 23 May 2024 17:56:34 GMT
• Title: Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling
• Authors: Liwen Wu, Sai Bi, Zexiang Xu, Fujun Luan, Kai Zhang, Iliyan Georgiev, Kalyan Sunkavalli, Ravi Ramamoorthi,
• Abstract summary: NDE transfers the concept of feature-grid-based spatial encoding to the angular domain. Experiments on both synthetic and real datasets show that a NeRF model with NDE outperforms the state of the art on view synthesis of specular objects.
• Score: 47.86734601629109
• License: http://creativecommons.org/licenses/by/4.0/
• 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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