Recoverability-Aware Gaussian Pruning for 3D Gaussian Splatting Compression

Gaussian pruning for 3D Gaussian Splatting (3DGS) typically retains primitives according to importance or estimated removal effect at the model state. Yet pruning quality is evaluated only after the remaining Gaussians are further optimized, leaving a key factor unmodeled: how well the retained representation adapts to removed information. We call this post-pruning recoverability. We study recoverability through controlled counterfactual interventions. Across matched recovery trajectories, recoverability exhibits reproducible structure across recovery schedules, with more consistent cross-schedule agreement at longer recovery horizons, while freezing retained Gaussians with strong geometric responses reduces subsequent recovery. Directly using this response as a pruning criterion, however, degrades reconstruction quality, showing that counterfactual compensation evidence complements rather than replaces primitive importance. Based on this observation, we introduce RAGP (Recoverability-Aware Gaussian Pruning), a fixed-budget refinement framework for Gaussian importance criteria. RAGP uses counterfactual pruning previews to measure the response of the surviving representation to localized information removal. The resulting geometric response is whitened and fused with the host importance prior through bounded host-relative calibration, while preserving the native host-score construction, Gaussian budget, and post-pruning optimization protocol. Thus, RAGP changes only the retained Gaussian identities, adding no parameters or inference-time rendering operations. Across five OMG pruning budgets and 13 scenes, RAGP improves mean PSNR by $0.061$–$0.148$ dB under matched Gaussian counts, consistently outperforming the pruning pipeline at the same compression budget.