UE4-NeRF: Neural radiance field for real-time rendering of large-scale scene

Document Type

Conference Proceeding

Publication Title

Advances in Neural Information Processing Systems 36 (NeurIPS 2023)

Publisher

NeurIPS

School

School of Science

RAS ID

64780

Funders

National Natural Science Foundation of China

Comments

Gu, J., Jiang, M., Li, H., Lu, X., Zhu, G., Shah, S. A. A., . . . Bennamoun, M. (2023). UE4-NeRF: Neural radiance field for real-time rendering of large-scale scene. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023) (pp. 1-13). NeurIPS. https://proceedings.neurips.cc/paper_files/paper/2023/hash/b94d8b035e2183e47afef9e2f299ba47-Abstract-Conference.html

Abstract

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/.

Access Rights

free_to_read

Link to Full Text

Share

 
COinS