From Geometry to Semantics: Efficient 3DGS Feature Compression with Geometry-Transferred Low-Rank Adaptation

3D Gaussian Splatting (3DGS) enables high-fidelity rendering by representing scenes with explicit Gaussian primitives, but its extension to semantic-aware representations introduces substantial storage overhead due to high-dimensional language-aligned features. Existing compression methods, designed primarily for geometric fidelity, fail to preserve task-relevant semantics under aggressive bitrate constraints, revealing a fundamental mismatch between signal reconstruction and feature geometry preservation. In this work, we revisit semantic compression from a task-oriented perspective and propose a rate–distortion optimized framework for semantic feature compression in 3DGS. Our key insight is that effective compression must preserve the geometry of language-aligned feature spaces rather than raw signal fidelity. To this end, we transfer geometric compression priors to the semantic domain via low-rank adaptation (LoRA), enabling parameter-efficient alignment without modifying the pre-trained backbone. Furthermore, we introduce a geometry-guided attention mechanism that leverages geometric latents as zero-cost side information to enhance semantic consistency during compression. The entire framework is trained end-to-end under a hyperprior-based entropy model, enabling explicit bitrate control. Extensive experiments show that our method achieves near-lossless downstream performance (mIoU 0.812 vs. 0.816 uncompressed) at a significantly reduced bitrate (3.67 bpe), while preserving downstream capabilities such as semantic retrieval and language-guided editing.