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 the reader to explore these models in person at our
project website: this https URL

SMERF 是一个高精度的实时视图合成方法，其采用分层模型划分方案和蒸馏训练策略，实现了对大场景的高保真和内部一致性，能够在网络浏览器上实现六自由度导航并在智能手机和笔记本电脑上实时渲染。