  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.
