title: HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces
summary: Neural radiance fields provide state-of-the-art view synthesis quality but
  tend to be slow to render. One reason is that they make use of volume
  rendering, thus requiring many samples (and model queries) per ray at render
  time. Although this representation is flexible and easy to optimize, most
  real-world objects can be modeled more efficiently with surfaces instead of
  volumes, requiring far fewer samples per ray. This observation has spurred
  considerable progress in surface representations such as signed distance
  functions, but these may struggle to model semi-opaque and thin structures. We
  propose a method, HybridNeRF, that leverages the strengths of both
  representations by rendering most objects as surfaces while modeling the
  (typically) small fraction of challenging regions volumetrically. We evaluate
  HybridNeRF against the challenging Eyeful Tower dataset along with other
  commonly used view synthesis datasets. When comparing to state-of-the-art
  baselines, including recent rasterization-based approaches, we improve error
  rates by 15-30% while achieving real-time framerates (at least 36 FPS) for
  virtual-reality resolutions (2Kx2K).
paper_authors._id: 657138bf670035a607452ee7
paper_authors.name: Haithem Turki
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/6702ba8fabe3d08884aa757f90cea333.svg
paper_authors.fullname: Haithem Turki
paper_authors.type: user
paper_authors.user: hturki
paper_authors._id: 657138bf670035a607452ee8
paper_authors.name: Vasu Agrawal
paper_authors.status: admin_assigned
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paper_authors.fullname: Vasu Agrawal
paper_authors.type: user
paper_authors.user: vasuagrawal4
paper_authors._id: 657138bf670035a607452ee9
paper_authors.name: Samuel Rota Bulò
paper_authors._id: 657138bf670035a607452eea
paper_authors.name: Lorenzo Porzi
paper_authors._id: 657138bf670035a607452eeb
paper_authors.name: Peter Kontschieder
paper_authors._id: 657138bf670035a607452eec
paper_authors.name: Deva Ramanan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-07T11:59:57.707Z
paper_authors._id: 6337151b0267ebcf02640eb6
paper_authors.avatarUrl: /avatars/14a723cafc5587043bdfb19304fc202d.svg
paper_authors.fullname: Deva Ramanan
paper_authors.type: user
paper_authors.user: devakramanan
paper_authors._id: 657138bf670035a607452eed
paper_authors.name: Michael Zollhöfer
paper_authors._id: 657138bf670035a607452eee
paper_authors.name: Christian Richardt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T20:36:57.610Z
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: HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces
paper_summary: Neural radiance fields provide state-of-the-art view synthesis quality but
  tend to be slow to render. One reason is that they make use of volume
  rendering, thus requiring many samples (and model queries) per ray at render
  time. Although this representation is flexible and easy to optimize, most
  real-world objects can be modeled more efficiently with surfaces instead of
  volumes, requiring far fewer samples per ray. This observation has spurred
  considerable progress in surface representations such as signed distance
  functions, but these may struggle to model semi-opaque and thin structures. We
  propose a method, HybridNeRF, that leverages the strengths of both
  representations by rendering most objects as surfaces while modeling the
  (typically) small fraction of challenging regions volumetrically. We evaluate
  HybridNeRF against the challenging Eyeful Tower dataset along with other
  commonly used view synthesis datasets. When comparing to state-of-the-art
  baselines, including recent rasterization-based approaches, we improve error
  rates by 15-30% while achieving real-time framerates (at least 36 FPS) for
  virtual-reality resolutions (2Kx2K).
paper_ai_keywords: neural radiance fields view synthesis volume rendering samples per ray signed distance functions rendering HybridNeRF surface representations volumetrically Eyeful Tower dataset real-time framerates