LG-HCC：局部几何感知的层次上下文压缩用于3D高斯点扩散
LG-HCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting
AI总结：
本文提出LG-HCC框架，通过引入局部几何感知的锚点修剪和分层熵编码，提升3D高斯点扩散的存储效率与几何一致性。
摘要：
尽管3D高斯点扩散（3DGS）能够实现高保真的实时渲染，但其巨大的存储开销严重阻碍了实际部署。最近的基于锚点的3DGS压缩方案通过一些先进的上下文模型减少高斯冗余，但忽略了显式的几何依赖关系，导致结构退化和次优的率失真性能。在本文中，我们提出了一种局部几何感知的层次上下文压缩框架用于3DGS（LG-HCC），该框架将锚点间的几何相关性纳入锚点修剪和熵编码中，以实现紧凑的表示。具体而言，我们引入了局部感知锚点修剪（NAAP）策略，通过加权邻域特征聚合评估锚点的重要性，然后将低贡献的锚点合并到显著的邻居中，从而得到一个紧凑且几何一致的锚点集。此外，我们进一步开发了一种分层熵编码方案，通过轻量级的几何引导卷积（GG-Conv）操作器利用粗到细的先验，以实现空间自适应的上下文建模和率失真优化。广泛的实验表明，LG-HCC有效缓解了结构保持问题，实现了优越的几何完整性和渲染保真度，同时在Mip-NeRF360数据集上相比Scaffold-GS基线将存储量减少了高达30.85倍。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
