The conventional mesh-based Level of Detail (LoD) technique, exemplified by applications such as Google Earth and many game engines, exhibits the capability to holistically represent a large scene even the Earth, and achieves rendering with a space complexity of O(log n). This constrained data requirement not only enhances rendering efficiency but also facilitates dynamic data fetching, thereby enabling a seamless 3D navigation experience for users. In this work, we extend this proven LoD technique to Neural Radiance Fields (NeRF) by introducing an octree structure to represent the scenes in different scales. This innovative approach provides a mathematically simple and elegant representation with a rendering space complexity of O(log n), aligned with the efficiency of mesh-based LoD techniques. We also present a novel training strategy that maintains a complexity of O(n). This strategy allows for parallel training with minimal overhead, ensuring the scalability and efficiency of our proposed method. Our contribution is not only in extending the capabilities of existing techniques but also in establishing a foundation for scalable and efficient large-scale scene representation using NeRF and octree structures.

翻译：传统基于网格的层次细节（LoD）技术，如Google Earth及众多游戏引擎中的应用，展现了整体表示大型场景（甚至包括地球）的能力，并以O(log n)的空间复杂度实现渲染。这种有限的数据需求不仅提升了渲染效率，还促进了动态数据获取，从而为用户带来无缝的三维导航体验。在本工作中，我们通过引入八叉树结构以不同尺度表示场景，将这一成熟的LoD技术扩展至神经辐射场（NeRF）。该创新方法提供了一种数学上简洁优雅的表示方式，其渲染空间复杂度为O(log n)，与基于网格的LoD技术效率一致。我们还提出了一种保持O(n)复杂度的新型训练策略。该策略能够以极小的开销实现并行训练，确保所提方法的可扩展性与效率。我们的贡献不仅在于扩展了现有技术的能力，更为基于NeRF和八叉树结构的可扩展、高效大规模场景表示奠定了基础。