提升3D高斯点云压缩效率，兼顾几何结构与渲染质量
LG-HCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting
- 通过邻域感知剪枝保留关键点，融合几何关联性
- 压缩率最高达30.85倍，保持高保真渲染效果
- 适合需要高效存储的3D内容部署场景
尽管3D高斯点云（3DGS）可实现高保真实时渲染，但其巨大的存储开销严重制约实际应用。现有基于锚点的压缩方法虽通过上下文模型减少冗余，却忽视显式几何依赖，导致结构退化和码率-失真性能不佳。本文提出局部几何感知层级压缩框架LG-HCC，将锚点间的几何相关性融入剪枝与熵编码，实现紧凑表示。具体地，设计邻域感知锚点剪枝（NAAP）策略，通过加权邻域特征聚合评估锚点重要性，将低贡献锚点合并至显著邻居，生成紧凑且几何一致的锚点集；进一步提出分层熵编码方案，利用轻量级几何引导卷积（GG-Conv）构建粗到细先验，实现空间自适应上下文建模与码率-失真优化。大量实验表明，LG-HCC有效缓解结构保持问题，在Mip-NeRF360数据集上相较Scaffold-GS基线压缩率达30.85倍，同时保持更优几何完整性和渲染保真度。
原文摘要 · Abstract (English)
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
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