Adaptive Voronoi NeRFs

TL;DR

This paper introduces an adaptive Voronoi-based hierarchical partitioning method for NeRFs, enabling faster scene learning and rendering by dividing the scene into simpler regions with individual NeRFs, improving efficiency and quality.

Contribution

It proposes a novel Voronoi diagram hierarchy for scene partitioning that enhances NeRF training and rendering speed while maintaining quality, adaptable to various NeRF variants.

Findings

Faster scene learning and rendering with Voronoi-based partitioning.

Even distribution of information improves training efficiency.

Adaptive refinement reduces artifacts and enhances quality.

Abstract

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.