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.

Block-NeRF 是神经辐射场的变种，可以有效表示大规模环境，将场景分解成 NeRF 可以将渲染时间与场景大小分离，使渲染可以扩展到任意大的环境，并允许环境进行每个块更新。通过引入外观嵌入，学习的姿态细化和可控的曝光，以及介绍邻接 NeRF 之间对齐外观的程序，我们采用数种架构变化使 NeRF 对不同环境条件下多个月采集的数据具有鲁棒性。通过 280 万张图像构建 Block-NeRF 的网格，创建迄今为止最大的神经场景表示，能够渲染旧金山整个社区。