PyNeRF: Pyramidal Neural Radiance Fields
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.
656d3fe379a834846b085a8d
Haithem Turki
claimed_verified
2023-12-05T08:40:40.392Z
656e000253703dd78fd072a9
/avatars/6702ba8fabe3d08884aa757f90cea333.svg
Haithem Turki
user
hturki
656d3fe379a834846b085a8e
Michael Zollhöfer
656d3fe379a834846b085a8f
Christian Richardt
claimed_verified
2025-02-13T20:36:59.426Z
67adc0cfe1f8b9375a6c7a63
/avatars/942d92ff9bd9e7a0f8f4687babbb8153.svg
Christian Richardt
user
cr333
656d3fe379a834846b085a90
Deva Ramanan
admin_assigned
2023-12-04T11:10:21.993Z
6337151b0267ebcf02640eb6
/avatars/14a723cafc5587043bdfb19304fc202d.svg
Deva Ramanan
user
devakramanan
PyNeRF: Pyramidal Neural Radiance Fields
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.
A modified grid-based approach improves NeRF rendering quality by training model heads at different resolutions, reducing aliasing artifacts and accelerating training.
Neural Radiance Fields (NeRFs) spatial grid representations aliasing artifacts volumetric frustums point samples positional encodings Mip-NeRF model heads spatial grid resolutions