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：传统的基于网格的细节层次技术，例如在 Google Earth 和众多游戏引擎中的应用，展现了完整表示大型场景（甚至地球）的能力，并能以 O(log n) 的空间复杂度实现渲染。这种受限的数据需求不仅提升了渲染效率，也促进了动态数据获取，从而为用户提供了无缝的 3D 导航体验。在本工作中，我们通过引入八叉树结构来在不同尺度下表示场景，将这种成熟的细节层次技术扩展至神经辐射场。这种创新方法提供了一种数学上简洁优雅的表示，其渲染空间复杂度为 O(log n)，与基于网格的细节层次技术效率相当。我们还提出了一种新颖的训练策略，其复杂度保持在 O(n)。该策略支持以最小开销进行并行训练，确保了我们所提方法的可扩展性和效率。我们的贡献不仅在于扩展了现有技术的能力，还在于为使用神经辐射场和八叉树结构实现可扩展、高效的大规模场景表示奠定了基础。