室外大场景神经辐射场综述

The 3D modeling of large outdoor scenes can not only complete real-time urban mapping and roaming, but also provide technical support for autonomous driving. In recent years, the advancement of neural implicit representation has been rapid, and the emergence of the neural radiance fields (NeRF) has propelled neural implicit representation to a new height. With its characteristics of high-quality rendering and arbitrary angle rendering, NeRF has been widely applied in controllable editing, digital human body, urban scene reconstruction, and other fields. The neural radiance field utilizes deep learning methods to learn implicit three-dimensional scenes from two-dimensional pictures and their poses, synthesizing novel view images. However, the original NeRF can only yield realistic results in bounded scenes, posing challenges in modeling large outdoor scenes due to problems such as unbounded backgrounds, model capacity constraints, and scene appearance. In order to deploy NeRF in large outdoor scenes, researchers have improved from multiple angles and proposed a variety of NeRF variants. Our review will begin by introducing the background of neural radiance fields, then delve into the challenges specific to the large outdoor scenes, analyzing and discussing the solutions to each, before concluding with a summary of current progress of NeRF for large outdoor scenes and prospects for the future.