Neural Radiance Fields (NeRFs) learn to represent a 3D scene from just a set of registered images. Increasing sizes of a scene demands more complex functions, typically represented by neural networks, to capture all details. Training and inference then involves querying the neural network millions of times per image, which becomes impractically slow. Since such complex functions can be replaced by multiple simpler functions to improve speed, we show that a hierarchy of Voronoi diagrams is a suitable choice to partition the scene. By equipping each Voronoi cell with its own NeRF, our approach is able to quickly learn a scene representation. We propose an intuitive partitioning of the space that increases quality gains during training by distributing information evenly among the networks and avoids artifacts through a top-down adaptive refinement. Our framework is agnostic to the underlying NeRF method and easy to implement, which allows it to be applied to various NeRF variants for improved learning and rendering speeds.

翻译：神经辐射场（NeRFs）能够仅通过一组已配准的图像学习表示三维场景。随着场景规模增大，通常由神经网络表示的复杂函数需要捕捉更多细节。训练和推理过程中每张图像需对神经网络进行数百万次查询，导致速度慢至不切实际。由于此类复杂函数可被多个简单函数替代以提升速度，我们证明Voronoi图层次结构是分割场景的合适选择。通过为每个Voronoi单元配备独立的NeRF，我们的方法能够快速学习场景表示。我们提出一种直观的空间划分方法，在训练过程中通过在各网络间均匀分布信息来提升质量增益，并通过自顶向下的自适应细化避免伪影。该框架与底层NeRF方法无关且易于实现，可应用于多种NeRF变体以提升学习与渲染速度。