RaPTGS: Render-Agnostic Post-Training Compression of 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) has emerged as a powerfultechnique for real-time, high-fidelity novel view synthesis. Despite theseadvantages, its multi-million point representation leads to large stor-age footprints that hinder asset distribution and on-device deployment.Existing compression approaches typically rely on access to training im-ages, camera poses, or the rendering pipeline for retraining or iterativefine-tuning. However, in realistic post-training and archival scenarios,often only the optimized model parameters are available. To addressthis limitation, we propose a render-agnostic, post-training compressionpipeline for 3DGS operating strictly under a model-only constraint. Wefirst introduce a camera-independent, multi-criteria importance score toprune redundant Gaussians based on a combination of geometric, spa-tial, and appearance-related cues. Following this pruning, a lightweight,training-free refinement step conservatively restores local coverage. Tofurther reduce the storage footprint, we apply degree-wise vector quanti-zation to the spherical harmonic coefficients and compress the remainingattributes via entropy coding. The compression performance is signifi-cantly enhanced by a custom spatial reordering to maximize local dataredundancy. Experiments on standard benchmarks show that our ap-proach achieves an average of 30× compression rate while maintainingcompetitive visual quality against existing methods.