REFINE: Super-efficient Pruning for 3D Gaussian Splatting via Rendering-Free Primitive Importance

Existing pruning methods for 3D Gaussian splatting (3DGS)suffer from either severe quality degradation or prohibitive computa-tional overhead. In this paper, we propose REFINE, a highly acceler-ated 3DGS pruning framework centered on a novel rendering-free primi-tive importance metric. Our approach leverages an analytically approx-imated, rendering-aware Hessian field to quantify the expected percep-tual error induced by the removal of individual primitives. By modelingthe joint modulation of visibility, projection geometry and the contentadaptive hyperparameter, we entirely bypass costly forward renderingpasses and derive an anisotropic perceptual weight field that serves asa high-fidelity proxy for primitive importance. Extensive experimentsacross multiple benchmark datasets demonstrate that REFINE main-tains highly competitive rendering quality while achieving a 3, 000× re-duction in pruning-related computational complexity, translating to apractical ∼ 20× speedup in device latency compared to state-of-the-artpruning methods.