  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.

Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics