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.
@article{arxiv.2311.05607,
title = {Real-Time Neural Rasterization for Large Scenes},
author = {Jeffrey Yunfan Liu and Yun Chen and Ze Yang and Jingkang Wang and Sivabalan Manivasagam and Raquel Urtasun},
journal= {arXiv preprint arXiv:2311.05607},
year = {2023}
}
Comments
Published in ICCV 2023. webpage: https://waabi.ai/NeuRas/