  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. We add appearance embeddings, learned pose
refinement, and controllable exposure to each individual NeRF, and introduce a
procedure for aligning appearance between adjacent NeRFs so that they can be
seamlessly combined. We build a grid of Block-NeRFs from 2.8 million images to
create the largest neural scene representation to date, capable of rendering an
entire neighborhood of San Francisco.
