Although 3D Gaussian Splatting (3DGS) enables high-fidelity real-time rendering, its prohibitive storage overhead severely hinders practical deployment. Recent anchor-based 3DGS compression schemes reduce gaussian redundancy through some advanced context models. However, they overlook explicit geometric dependencies, leading to structural degradation and suboptimal ratedistortion performance. In this paper, we propose a Local Geometry-aware Hierarchical Context Compression framework for 3DGS(LG-HCC) that incorporates inter-anchor geometric correlations into anchor pruning and entropy coding for compact representation. Specifically, we introduce an Neighborhood-Aware Anchor Pruning (NAAP) strategy, which evaluates anchor importance via weighted neighborhood feature aggregation and then merges low-contribution anchors into salient neighbors, yielding a compact yet geometry-consistent anchor set. Moreover, we further develop a hierarchical entropy coding scheme, in which coarse-to-fine priors are exploited through a lightweight Geometry-Guided Convolution(GG-Conv) operator to enable spatially adaptive context modeling and rate-distortion optimization. Extensive experiments show that LG-HCC effectively alleviates structural preservation issues,achieving superior geometric integrity and rendering fidelity while reducing storage by up to 30.85x compared to the Scaffold-GS baseline on the Mip-NeRF360 dataset

翻译：尽管三维高斯泼溅（3DGS）能够实现高保真实时渲染，但其高昂的存储开销严重阻碍了实际部署。近期基于锚点的3DGS压缩方案通过一些先进的上下文模型降低了高斯冗余，然而它们忽略了显式的几何依赖关系，导致结构退化与率失真性能次优。本文提出一种面向三维高斯泼溅的局部几何感知层次化上下文压缩框架（LG-HCC），该框架将锚点间几何相关性融入锚点剪枝与熵编码中，以实现紧凑表示。具体而言，我们引入邻域感知锚点剪枝（NAAP）策略，通过加权邻域特征聚合评估锚点重要性，并将低贡献锚点合并至显著邻居，从而生成紧凑且几何一致的锚点集合。此外，我们进一步开发了层次化熵编码方案，通过轻量级几何引导卷积（GG-Conv）算子利用由粗到精的先验信息，实现空间自适应上下文建模与率失真优化。大量实验表明，LG-HCC有效缓解了结构保持问题，在Mip-NeRF360数据集上，相较于Scaffold-GS基线，在实现更优几何完整性与渲染保真度的同时，将存储开销最高降低30.85倍。