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）通过仅使用一组注册的图像来学习表示3D场景。随着场景尺寸的增加，需要更复杂的函数才能捕捉所有细节，通常使用神经网络表示。训练和推断需要针对每个图像查询神经网络数百万次，这变得不实用。由于可以通过多个简单函数替换这些复杂函数以提高速度，因此我们表明分层 Voronoi 图在分隔场景方面是合适的选择。通过为每个 Voronoi 单元格配备自己的 NeRF，我们的方法能够快速学习场景表示。我们提出了一种直观的空间划分方法，通过在网络之间均匀分配信息以在训练过程中获得质量收益，并通过自上而下的自适应细化避免伪影。我们的框架对底层的 NeRF 方法不加关注，易于实现，可应用于各种 NeRF 变体以改善学习和渲染速度。