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。

Block-NeRF:可扩展的大场景神经视角合成