LG-HCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting

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 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

Figures (6)

• Figure 1: Trade-off between fidelity (PSNR) and rendering speed (FPS) for different methods on the BungeeNeRF dataset xiangli2022bungeenerf. Circle size denotes storage cost, where smaller circles correspond to lower storage.
• Figure 2: Overview of the LG-HCC framework. Following Colmap initialization and densification, Neighborhood-Aware Anchor Pruning (NAAP) adaptively merges low-contribution anchors into salient neighbors via a local graph to ensure geometric consistency. The preserved anchors then undergo Hierarchical Geometry-Guided Context Modeling for efficient entropy coding. Finally, decoded attributes generate 3D Gaussians via learnable offsets for high-fidelity rasterization.
• Figure 3: Neighborhood-Aware Anchor Pruning (NAAP) pipeline starts by constructing an anchor-based geometric graph, with nodes colored by average opacity (darker = higher). Importance scores ($\xi_i$) are computed via neighborhood aggregation. Low-score anchors are identified as redundant and associated with their nearest salient neighbor (Local Neighbor Preserver). Instead of direct removal, a Weighted Attribute Transformation merges attributes from pruned anchors (opacity $\bar{\alpha}_i$, scaling $s_i$, offset $o_i$) into the nearest retained anchor, yielding fused attributes ($\phi_i$, $O_i$, $S_i$, etc.) to preserve geometric and appearance in a compact structure.
• Figure 4: This module exploits cross-level correlations by mapping Level 1 attributes to Level 2 query anchors via a deterministic mapping $\mathcal{M}$ to obtain the preliminary attributes $\mathcal{A}_{pre}^{(2)}$. Then, a $k$-NN graph is constructed over $\mathcal{A}_{pre}^{(2)}$ to form the graph-based geometric prior $\mathcal{G}_{geo}^{(2)}$. Within the GG-Conv (dashed box): (1) Geometry branch: Relative offsets $\Delta \mathbf{p}_{ij}$ query a learnable 3D weight table via trilinear interpolation to generate dynamic weights $\mathbf{w}_{ij}$ encoding the spatial layout. (2) Feature branch: Residual features $\Delta \mathbf{f}_{ij}^{(1)}$ and offsets $\Delta \mathbf{p}_{ij}$ are transformed by MLP $\phi$ into embeddings $\mathbf{e}_{ij}$. Finally, $\mathbf{w}_{ij}$ modulates $\mathbf{e}_{ij}$ to produce the local geometry-aware feature $\mathbf{h}_i$, which serves as the final context $\mathbf{a}_{i(ctx)}$ for Level 2 entropy coding.
• Figure 5: Qualitative results of the proposed LG-HCC method compared to existing three compression methods on the drjohnson (DeepBlending), train (Tank&Temples) and bicycile (Mip-NeRF360) scenes, respectively.