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/

翻译：三维高斯泼溅技术近期已成为静态三维场景建模中极具前景的技术。与神经辐射场不同，该技术利用高效光栅化方法实现高质量快速渲染。然而，其存储体积显著增大，这阻碍了在资源受限设备等场景中的实际部署。本文提出一种紧凑场景表示方法，将三维高斯泼溅（3DGS）参数组织为具有局部同质性的二维网格，在保证渲染视觉质量的同时大幅降低存储需求。其核心思想在于显式利用自然场景中存在的感知冗余性——场景的本质特性允许通过高斯参数的多种排列组合实现等效表示。为此，我们提出一种新型高度并行算法，将高维高斯参数规则排列为二维网格，同时保持其邻域结构。训练过程中，我们进一步强化网格内排序参数间的局部平滑性。未压缩的高斯参数保持与3DGS相同的结构，确保与现有渲染器的无缝集成。本方法在保持训练时间不变的前提下，对复杂场景实现17至42倍的存储压缩比，标志着三维场景分发与消费领域的重大突破。更多信息参见项目页面：https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/