  Novel view synthesis has seen major advances in recent years, with 3D
Gaussian splatting offering an excellent level of visual quality, fast training
and real-time rendering. However, the resources needed for training and
rendering inevitably limit the size of the captured scenes that can be
represented with good visual quality. We introduce a hierarchy of 3D Gaussians
that preserves visual quality for very large scenes, while offering an
efficient Level-of-Detail (LOD) solution for efficient rendering of distant
content with effective level selection and smooth transitions between levels.We
introduce a divide-and-conquer approach that allows us to train very large
scenes in independent chunks. We consolidate the chunks into a hierarchy that
can be optimized to further improve visual quality of Gaussians merged into
intermediate nodes. Very large captures typically have sparse coverage of the
scene, presenting many challenges to the original 3D Gaussian splatting
training method; we adapt and regularize training to account for these issues.
We present a complete solution, that enables real-time rendering of very large
scenes and can adapt to available resources thanks to our LOD method. We show
results for captured scenes with up to tens of thousands of images with a
simple and affordable rig, covering trajectories of up to several kilometers
and lasting up to one hour. Project Page:
https://repo-sam.inria.fr/fungraph/hierarchical-3d-gaussians/
