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/.

翻译：神经辐射场（NeRF）是一种新型隐式三维重建方法，展现出巨大潜力并受到日益关注。它能够仅从一组照片中重建三维场景。然而，其实时渲染能力，尤其是针对大规模场景的交互式实时渲染，仍存在显著局限性。为解决这些挑战，本文提出了一种名为UE4-NeRF的新型神经渲染系统，专门用于大规模场景的实时渲染。我们将每个大型场景划分为不同的子NeRF。为表示这些划分后的独立场景，我们通过在场景内构建多个正则八面体来初始化多边形网格，并在训练过程中持续优化多边形面的顶点。借鉴层次细节（LOD）技术的思想，我们针对不同观测层级训练了不同细节层次的网格。该方法结合虚幻引擎4（UE4）中的光栅化管线，实现了大规模场景在4K分辨率下帧率高达43 FPS的实时渲染。在UE4内进行渲染还便于后续阶段的场景编辑。此外，实验表明，我们的方法达到了与最先进方法相媲美的渲染质量。项目页面：https://jamchaos.github.io/UE4-NeRF/。