  3D Gaussian Splatting has recently emerged as a highly promising technique
for modeling of static 3D scenes. In contrast to Neural Radiance Fields, it
utilizes efficient rasterization allowing for very fast rendering at
high-quality. However, the storage size is significantly higher, which hinders
practical deployment, e.g. on resource constrained devices. In this paper, we
introduce a compact scene representation organizing the parameters of 3D
Gaussian Splatting (3DGS) into a 2D grid with local homogeneity, ensuring a
drastic reduction in storage requirements without compromising visual quality
during rendering. Central to our idea is the explicit exploitation of
perceptual redundancies present in natural scenes. In essence, the inherent
nature of a scene allows for numerous permutations of Gaussian parameters to
equivalently represent it. To this end, we propose a novel highly parallel
algorithm that regularly arranges the high-dimensional Gaussian parameters into
a 2D grid while preserving their neighborhood structure. During training, we
further enforce local smoothness between the sorted parameters in the grid. The
uncompressed Gaussians use the same structure as 3DGS, ensuring a seamless
integration with established renderers. Our method achieves a reduction factor
of 17x to 42x in size for complex scenes with no increase in training time,
marking a substantial leap forward in the domain of 3D scene distribution and
consumption. Additional information can be found on our project page:
https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/
