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）能够仅从一组已配准图像学习并重建三维场景。随着场景规模增大，需要更复杂的函数（通常由神经网络表示）来捕获所有细节。训练和推理过程中，每幅图像需要对神经网络进行数百万次查询，这变得不切实际地缓慢。由于此类复杂函数可被多个简单函数替代以提高速度，我们证明Voronoi图的层级结构是划分场景的合适选择。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策略：在训练过程中通过在各网络间均匀分布信息提升质量增益，并通过自上而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，因此可应用于多种NeRF变体，以提升学习与渲染速度。