We use neural radiance fields (NeRFs) to build interactive 3D environments
from large-scale visual captures spanning buildings or even multiple city
blocks collected primarily from drones. In contrast to single object scenes (on
which NeRFs are traditionally evaluated), our scale poses multiple challenges
including (1) the need to model thousands of images with varying lighting
conditions, each of which capture only a small subset of the scene, (2)
prohibitively large model capacities that make it infeasible to train on a
single GPU, and (3) significant challenges for fast rendering that would enable
interactive fly-throughs.
To address these challenges, we begin by analyzing visibility statistics for
large-scale scenes, motivating a sparse network structure where parameters are
specialized to different regions of the scene. We introduce a simple geometric
clustering algorithm for data parallelism that partitions training images (or
rather pixels) into different NeRF submodules that can be trained in parallel.
We evaluate our approach on existing datasets (Quad 6k and UrbanScene3D) as
well as against our own drone footage, improving training speed by 3x and PSNR
by 12%. We also evaluate recent NeRF fast renderers on top of Mega-NeRF and
introduce a novel method that exploits temporal coherence. Our technique
achieves a 40x speedup over conventional NeRF rendering while remaining within
0.8 db in PSNR quality, exceeding the fidelity of existing fast renderers.

利用神经辐射场（NeRFs）建立大规模的高楼或多个城市街区跨度的交互式 3D 环境，研究通过分析可见性统计信息，提出特殊化参数不同区域的稀疏网络结构、基于几何聚类的数据并行算法，并对 NeRF 快速渲染程序提出了新方法，有效解决了训练速度和渲染速度等多项挑战。