title: PyNeRF: Pyramidal Neural Radiance Fields
summary: Neural Radiance Fields (NeRFs) can be dramatically accelerated by spatial
  grid representations. However, they do not explicitly reason about scale and so
  introduce aliasing artifacts when reconstructing scenes captured at different
  camera distances. Mip-NeRF and its extensions propose scale-aware renderers
  that project volumetric frustums rather than point samples but such approaches
  rely on positional encodings that are not readily compatible with grid methods.
  We propose a simple modification to grid-based models by training model heads
  at different spatial grid resolutions. At render time, we simply use coarser
  grids to render samples that cover larger volumes. Our method can be easily
  applied to existing accelerated NeRF methods and significantly improves
  rendering quality (reducing error rates by 20-90% across synthetic and
  unbounded real-world scenes) while incurring minimal performance overhead (as
  each model head is quick to evaluate). Compared to Mip-NeRF, we reduce error
  rates by 20% while training over 60x faster.
paper_authors._id: 656d3fe379a834846b085a8d
paper_authors.name: Haithem Turki
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-05T08:40:40.392Z
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: 656d3fe379a834846b085a8e
paper_authors.name: Michael Zollhöfer
paper_authors._id: 656d3fe379a834846b085a8f
paper_authors.name: Christian Richardt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T20:36:59.426Z
paper_authors._id: 67adc0cfe1f8b9375a6c7a63
paper_authors.avatarUrl: /avatars/942d92ff9bd9e7a0f8f4687babbb8153.svg
paper_authors.fullname: Christian Richardt
paper_authors.type: user
paper_authors.user: cr333
paper_authors._id: 656d3fe379a834846b085a90
paper_authors.name: Deva Ramanan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T11:10:21.993Z
paper_authors._id: 6337151b0267ebcf02640eb6
paper_authors.avatarUrl: /avatars/14a723cafc5587043bdfb19304fc202d.svg
paper_authors.fullname: Deva Ramanan
paper_authors.type: user
paper_authors.user: devakramanan
paper_title: PyNeRF: Pyramidal Neural Radiance Fields
paper_summary: Neural Radiance Fields (NeRFs) can be dramatically accelerated by spatial
  grid representations. However, they do not explicitly reason about scale and so
  introduce aliasing artifacts when reconstructing scenes captured at different
  camera distances. Mip-NeRF and its extensions propose scale-aware renderers
  that project volumetric frustums rather than point samples but such approaches
  rely on positional encodings that are not readily compatible with grid methods.
  We propose a simple modification to grid-based models by training model heads
  at different spatial grid resolutions. At render time, we simply use coarser
  grids to render samples that cover larger volumes. Our method can be easily
  applied to existing accelerated NeRF methods and significantly improves
  rendering quality (reducing error rates by 20-90% across synthetic and
  unbounded real-world scenes) while incurring minimal performance overhead (as
  each model head is quick to evaluate). Compared to Mip-NeRF, we reduce error
  rates by 20% while training over 60x faster.
paper_ai_keywords: Neural Radiance Fields (NeRFs) spatial grid-representations alias overhead aliasing artifacts Mip-NeRF scale-aware renderers volumetric frustums point samples positional encodings grid-based models modelheads spatial grid resolutions rendering quality error rates synthetic unbounded real-world scenes quick to evaluate