[2603.28431] GeoHCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.28431 (cs) [Submitted on 30 Mar 2026] Title:GeoHCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting Authors:Xuan Deng, Xiandong Meng, Hengyu Man, Qiang Zhu, Tiange Zhang, Debin Zhao, Xiaopeng Fan View a PDF of the paper titled GeoHCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting, by Xuan Deng and 6 other authors View PDF HTML (experimental) Abstract: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 redundancy through context modeling, yet overlook explicit geometric dependencies, leading to structural degradation and suboptimal rate-distortion performance. In this paper, we propose GeoHCC, a geometry-aware 3DGS compression framework that incorporates inter-anchor geometric correlations into anchor pruning and entropy coding for compact representation. We first introduce Neighborhood-Aware Anchor Pruning (NAAP), which evaluates anchor importance via weighted neighborhood feature aggregation and merges redundant anchors into salient neighbors, yielding a compact yet geometry-consistent anchor set. Building upon this optimized structure, we further develop a hierarchical entropy coding scheme, in which coarse-to-fine priors are exploited through a lightweight Geometry-Guided Convolution (G...