UE4-NeRF:Neural Radiance Field for Real-Time Rendering of Large-Scale
  Scene
Jiaming Gu Minchao Jiang Hongsheng Li Xiaoyuan Lu Guangming Zhu Syed Afaq Ali Shah Liang Zhang Mohammed Bennamoun
Neural Radiance Fields (NeRF) is a novel implicit 3D reconstruction method
that shows immense potential and has been gaining increasing attention. It
enables the reconstruction of 3D scenes solely from a set of photographs.
However, its real-time rendering capability, especially for interactive
real-time rendering of large-scale scenes, still has significant limitations.
To address these challenges, in this paper, we propose a novel neural rendering
system called UE4-NeRF, specifically designed for real-time rendering of
large-scale scenes. We partitioned each large scene into different sub-NeRFs.
In order to represent the partitioned independent scene, we initialize
polygonal meshes by constructing multiple regular octahedra within the scene
and the vertices of the polygonal faces are continuously optimized during the
training process. Drawing inspiration from Level of Detail (LOD) techniques, we
trained meshes of varying levels of detail for different observation levels.
Our approach combines with the rasterization pipeline in Unreal Engine 4 (UE4),
achieving real-time rendering of large-scale scenes at 4K resolution with a
frame rate of up to 43 FPS. Rendering within UE4 also facilitates scene editing
in subsequent stages. Furthermore, through experiments, we have demonstrated
that our method achieves rendering quality comparable to state-of-the-art
approaches. Project page: https://jamchaos.github.io/UE4-NeRF/.