3D Gaussian Splatting (GS) demonstrates excellent rendering quality and generation speed in novel view synthesis. However, substantial data size poses challenges for storage and transmission, making 3D GS compression an essential technology. Current 3D GS compression research primarily focuses on developing more compact scene representations, such as converting explicit 3D GS data into implicit forms. In contrast, compression of the GS data itself has hardly been explored. To address this gap, we propose a Hierarchical GS Compression (HGSC) technique. Initially, we prune unimportant Gaussians based on importance scores derived from both global and local significance, effectively reducing redundancy while maintaining visual quality. An Octree structure is used to compress 3D positions. Based on the 3D GS Octree, we implement a hierarchical attribute compression strategy by employing a KD-tree to partition the 3D GS into multiple blocks. We apply farthest point sampling to select anchor primitives within each block and others as non-anchor primitives with varying Levels of Details (LoDs). Anchor primitives serve as reference points for predicting non-anchor primitives across different LoDs to reduce spatial redundancy. For anchor primitives, we use the region adaptive hierarchical transform to achieve near-lossless compression of various attributes. For non-anchor primitives, each is predicted based on the k-nearest anchor primitives. To further minimize prediction errors, the reconstructed LoD and anchor primitives are combined to form new anchor primitives to predict the next LoD. Our method notably achieves superior compression quality and a significant data size reduction of over 4.5 times compared to the state-of-the-art compression method on small scenes datasets
3D Gaussian Splatting (GS) 在新视图合成中表现出卓越的渲染质量和生成速度。然而，其巨大的数据规模对存储和传输提出了挑战，使得 3D GS 数据压缩成为一项关键技术。目前的 3D GS 压缩研究主要集中于开发更紧凑的场景表示形式，例如将显式的 3D GS 数据转化为隐式形式。而对 GS 数据本身的压缩尚未得到充分探索。 为填补这一空白，我们提出了一种 分层高斯分布压缩技术 (Hierarchical GS Compression, HGSC)。首先，我们通过基于全局和局部重要性得分的筛选机制裁剪不重要的高斯基元，减少冗余同时保持视觉质量。随后，我们采用八叉树 (Octree) 结构对 3D 位置进行压缩。在基于 3D GS 八叉树的基础上，我们通过使用 KD 树将 3D GS 数据分块，实施分层属性压缩策略。在每个块内，我们通过最远点采样选取锚点基元 (anchor primitives)，其余为不同细节级别 (Levels of Details, LoDs) 的非锚点基元。 锚点基元作为参考点，用于预测不同 LoD 的非锚点基元，以减少空间冗余。对于锚点基元，我们采用区域自适应分层变换技术，实现对多种属性的近无损压缩。而对于非锚点基元，我们基于 k 个最近锚点基元进行预测。为进一步减少预测误差，结合重建的 LoD 和锚点基元形成新的锚点基元，进一步预测下一级 LoD。 实验表明，我们的方法在小规模场景数据集上实现了显著的数据压缩效果，与最先进的压缩方法相比，数据规模减少了超过 4.5 倍，同时保持了优越的压缩质量。