title: SpecNeRF: Gaussian Directional Encoding for Specular Reflections
summary: Neural radiance fields have achieved remarkable performance in modeling the
  appearance of 3D scenes. However, existing approaches still struggle with the
  view-dependent appearance of glossy surfaces, especially under complex lighting
  of indoor environments. Unlike existing methods, which typically assume distant
  lighting like an environment map, we propose a learnable Gaussian directional
  encoding to better model the view-dependent effects under near-field lighting
  conditions. Importantly, our new directional encoding captures the
  spatially-varying nature of near-field lighting and emulates the behavior of
  prefiltered environment maps. As a result, it enables the efficient evaluation
  of preconvolved specular color at any 3D location with varying roughness
  coefficients. We further introduce a data-driven geometry prior that helps
  alleviate the shape radiance ambiguity in reflection modeling. We show that our
  Gaussian directional encoding and geometry prior significantly improve the
  modeling of challenging specular reflections in neural radiance fields, which
  helps decompose appearance into more physically meaningful components.
paper_authors._id: 6583bbd225bb2d7767f2048a
paper_authors.name: Li Ma
paper_authors._id: 6583bbd225bb2d7767f2048b
paper_authors.name: Vasu Agrawal
paper_authors.status: admin_assigned
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paper_authors.fullname: Vasu Agrawal
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paper_authors.user: vasuagrawal4
paper_authors._id: 6583bbd225bb2d7767f2048c
paper_authors.name: Haithem Turki
paper_authors.status: admin_assigned
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paper_authors._id: 656e000253703dd78fd072a9
paper_authors.avatarUrl: /avatars/6702ba8fabe3d08884aa757f90cea333.svg
paper_authors.fullname: Haithem Turki
paper_authors.type: user
paper_authors.user: hturki
paper_authors._id: 6583bbd225bb2d7767f2048d
paper_authors.name: Changil Kim
paper_authors._id: 6583bbd225bb2d7767f2048e
paper_authors.name: Chen Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-21T09:09:58.998Z
paper_authors._id: 65049f41c6ae3df8f28cfe96
paper_authors.avatarUrl: /avatars/a653ed4a215c4bd34710a1cee9f4d9cd.svg
paper_authors.fullname: Chen Gao
paper_authors.type: user
paper_authors.user: gaochen315
paper_authors._id: 6583bbd225bb2d7767f2048f
paper_authors.name: Pedro Sander
paper_authors._id: 6583bbd225bb2d7767f20490
paper_authors.name: Michael Zollhöfer
paper_authors._id: 6583bbd225bb2d7767f20491
paper_authors.name: Christian Richardt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T20:36:54.044Z
paper_authors._id: 67adc0cfe1f8b9375a6c7a63
paper_authors.avatarUrl: /avatars/942d92ff9bd9e7a0f8f4687babbb8153.svg
paper_authors.fullname: Christian Richardt
paper_authors.type: user
paper_authors.user: cr333
paper_title: SpecNeRF: Gaussian Directional Encoding for Specular Reflections
paper_summary: Neural radiance fields have achieved remarkable performance in modeling the
  appearance of 3D scenes. However, existing approaches still struggle with the
  view-dependent appearance of glossy surfaces, especially under complex lighting
  of indoor environments. Unlike existing methods, which typically assume distant
  lighting like an environment map, we propose a learnable Gaussian directional
  encoding to better model the view-dependent effects under near-field lighting
  conditions. Importantly, our new directional encoding captures the
  spatially-varying nature of near-field lighting and emulates the behavior of
  prefiltered environment maps. As a result, it enables the efficient evaluation
  of preconvolved specular color at any 3D location with varying roughness
  coefficients. We further introduce a data-driven geometry prior that helps
  alleviate the shape radiance ambiguity in reflection modeling. We show that our
  Gaussian directional encoding and geometry prior significantly improve the
  modeling of challenging specular reflections in neural radiance fields, which
  helps decompose appearance into more physically meaningful components.
paper_ai_keywords: neural radiance fields Prompt appearance Glossy surfaces Complex lighting Indoor directional encoding Gaussian directional encoding View-dependent effects Near-field lighting Prefiltered environment maps Spatially-varying nature Varying roughness coefficients Data-driven geometry prior Shape radiance inversion Reflection modeling Physically meaningful components