Residual-Autoregressive Context for 3D Gaussian Splatting Compression

3D Gaussian Splatting (3DGS) has emerged as a promising method that enables fast and high-quality novel-view rendering. However, its large parameter size remains a bottleneck for storage and transmission. We propose *RacerGS*, a compact 3DGS compression framework that achieves scene reconstruction at significantly reduced storage sizes with preserved fidelity. To obtain compact and spatially coherent features, we design a hash projection model that projects the anchor hash encodings into compact context features. Additionally, we introduce a FiLM-modulated autoregressive GRU context model that utilizes causal dependencies among anchor feature groups and uses recurrent hidden states to refine entropy-model parameters, improving bitrate efficiency. Furthermore, we adaptively entropy-code quantized residuals of anchor attributes around their context-predicted means, reducing symbol entropy by leveraging spatial consistency. Overall, *RacerGS* achieves more than **$\mathbf{150}\boldsymbol{\times}$** compression over vanilla 3DGS and **$\mathbf{28}\boldsymbol{\times}$** over Scaffold-GS, while providing comparable rendering quality. Extensive experiments across five datasets demonstrate our superiority over prior 3DGS compression methods in both storage reduction and rate-distortion performance.