Block-NeRF: Scalable Large Scene Neural View Synthesis

Understand

We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments.

• Specifically, we demonstrate that when scaling NeRF to render city-scale scenes spanning multiple blocks, it is vital to decompose the scene into individually trained NeRFs.
• This decomposition decouples rendering time from scene size, enables rendering to scale to arbitrarily large environments, and allows per-block updates of the environment.
• We adopt several architectural changes to make NeRF robust to data captured over months under different environmental conditions.