  Recent techniques for real-time view synthesis have rapidly advanced in
fidelity and speed, and modern methods are capable of rendering
near-photorealistic scenes at interactive frame rates. At the same time, a
tension has arisen between explicit scene representations amenable to
rasterization and neural fields built on ray marching, with state-of-the-art
instances of the latter surpassing the former in quality while being
prohibitively expensive for real-time applications. In this work, we introduce
SMERF, a view synthesis approach that achieves state-of-the-art accuracy among
real-time methods on large scenes with footprints up to 300 m$^2$ at a
volumetric resolution of 3.5 mm$^3$. Our method is built upon two primary
contributions: a hierarchical model partitioning scheme, which increases model
capacity while constraining compute and memory consumption, and a distillation
training strategy that simultaneously yields high fidelity and internal
consistency. Our approach enables full six degrees of freedom (6DOF) navigation
within a web browser and renders in real-time on commodity smartphones and
laptops. Extensive experiments show that our method exceeds the current
state-of-the-art in real-time novel view synthesis by 0.78 dB on standard
benchmarks and 1.78 dB on large scenes, renders frames three orders of
magnitude faster than state-of-the-art radiance field models, and achieves
real-time performance across a wide variety of commodity devices, including
smartphones. We encourage readers to explore these models interactively at our
project website: https://smerf-3d.github.io.
