We propose a new method for realistic real-time novel-view synthesis (NVS) of
large scenes. Existing neural rendering methods generate realistic results, but
primarily work for small scale scenes (<50 square meters) and have difficulty
at large scale (>10000 square meters). Traditional graphics-based rasterization
rendering is fast for large scenes but lacks realism and requires expensive
manually created assets. Our approach combines the best of both worlds by
taking a moderate-quality scaffold mesh as input and learning a neural texture
field and shader to model view-dependant effects to enhance realism, while
still using the standard graphics pipeline for real-time rendering. Our method
outperforms existing neural rendering methods, providing at least 30x faster
rendering with comparable or better realism for large self-driving and drone
scenes. Our work is the first to enable real-time rendering of large real-world
scenes.

我们提出了一种逼真的实时小说视角综合（NVS）大场景的新方法，通过将中等质量的脚手架网格作为输入，学习神经纹理场和着色器以增强逼真感，同时使用标准图形管道进行实时渲染，我们的方法在大型自动驾驶和无人机场景中提供至少 30 倍更快的渲染速度与可比或更好的逼真度，我们的工作是首次实现大型真实世界场景的实时渲染。