PyNeRF: Pyramidal Neural Radiance Fields
Haithem Turki Michael Zollhöfer Christian Richardt Deva Ramanan
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.