  With the advent of Neural Radiance Fields (NeRF), neural networks can now
render novel views of a 3D scene with quality that fools the human eye. Yet,
generating these images is very computationally intensive, limiting their
applicability in practical scenarios. In this paper, we propose a technique
based on spatial decomposition capable of mitigating this issue. Our key
observation is that there are diminishing returns in employing larger (deeper
and/or wider) networks. Hence, we propose to spatially decompose a scene and
dedicate smaller networks for each decomposed part. When working together,
these networks can render the whole scene. This allows us near-constant
inference time regardless of the number of decomposed parts. Moreover, we show
that a Voronoi spatial decomposition is preferable for this purpose, as it is
provably compatible with the Painter's Algorithm for efficient and GPU-friendly
rendering. Our experiments show that for real-world scenes, our method provides
up to 3x more efficient inference than NeRF (with the same rendering quality),
or an improvement of up to 1.0~dB in PSNR (for the same inference cost).
