1. Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling
- Author
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Wu, Liwen, Bi, Sai, Xu, Zexiang, Luan, Fujun, Zhang, Kai, Georgiev, Iliyan, Sunkavalli, Kalyan, and Ramamoorthi, Ravi
- Subjects
Computer Science - Computer Vision and Pattern Recognition - 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/}., Comment: Accepted to CVPR 2024
- Published
- 2024