title: Neural Directional Encoding for Efficient and Accurate View-Dependent
    Appearance Modeling
summary: 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/.
paper_authors._id: 66509bf987f3322826fb775f
paper_authors.name: Liwen Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-24T15:17:51.657Z
paper_authors._id: 662b680ef6a60e8e58b15673
paper_authors.avatarUrl: /avatars/b1f4636511903092cf6bf286cedb600c.svg
paper_authors.fullname: liwenwu
paper_authors.type: user
paper_authors.user: liwenwu
paper_authors._id: 66509bf987f3322826fb7760
paper_authors.name: Sai Bi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-24T15:17:58.101Z
paper_authors._id: 66303560597dc7c535168f07
paper_authors.avatarUrl: /avatars/a69d52a4a198c6c8451c1d137031bf07.svg
paper_authors.fullname: Sai Bi
paper_authors.type: user
paper_authors.user: saibi
paper_authors._id: 66509bf987f3322826fb7761
paper_authors.name: Zexiang Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-24T15:18:05.983Z
paper_authors._id: 65565b1b8616d8d740abb976
paper_authors.avatarUrl: /avatars/c245b19ed05292bf1bf4a9d8cf2a409c.svg
paper_authors.fullname: Zexiang Xu
paper_authors.type: user
paper_authors.user: zexiangxu
paper_authors._id: 66509bf987f3322826fb7762
paper_authors.name: Fujun Luan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-24T15:18:12.137Z
paper_authors._id: 662ae0aa4f711ee4e1e17ffc
paper_authors.avatarUrl: /avatars/2372544e8e5cfeb60a052c793f5e2522.svg
paper_authors.fullname: Fujun Luan
paper_authors.type: user
paper_authors.user: luanfujun
paper_authors._id: 66509bf987f3322826fb7763
paper_authors.name: Kai Zhang
paper_authors._id: 66509bf987f3322826fb7764
paper_authors.name: Iliyan Georgiev
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-24T15:18:47.186Z
paper_authors._id: 651aea3ec6d84a147b1c6ade
paper_authors.avatarUrl: /avatars/c387b90d618d195d0338b89b2d0bcfea.svg
paper_authors.fullname: Iliyan Georgiev
paper_authors.type: user
paper_authors.user: iliyan-g
paper_authors._id: 66509bf987f3322826fb7765
paper_authors.name: Kalyan Sunkavalli
paper_authors._id: 66509bf987f3322826fb7766
paper_authors.name: Ravi Ramamoorthi
paper_title: Neural Directional Encoding for Efficient and Accurate View-Dependent
    Appearance Modeling
paper_summary: 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/.
paper_ai_keywords: Neural Directional Encoding (NDE) neural radiance fields (NeRF) feature-grid-based spatial encoding high-frequency angular signals cone-trace spatial features interreflection effects view synthesis specular objects global illumination effects real-time inference