VanishGS: Dual-Path Modeling of Primitive Disappearance for Pruning 3D Gaussian Splatting

While 3D Gaussian Splatting (3DGS) enables high-quality real-time novel view synthesis, its reliance on millions of Gaussian primitives leads to substantial storage and computational overhead. Existing post-pruning methods typically estimate primitive importance from rendering contribution, heuristic attributes, or generic parameter sensitivity, which do not directly measure the rendering distortion caused by removing a Gaussian, potentially leading to inaccurate importance estimation. In this paper, we propose VanishGS, a post-pruning framework that estimates Gaussian importance by the rendering distortion induced by its disappearance. To capture this effect, we model Gaussian disappearance through two paths, opacity dissolution and geometric collapse, characterizing its rendering impact from both appearance and geometric perspectives. We estimate the directional distortion along these pruning perturbations using the Gauss–Newton matrix and efficiently approximate the required diagonal entries through randomized projections. By measuring the cost of removing each Gaussian, VanishGS provides an importance criterion that is better aligned with the pruning objective. Extensive experiments across multiple benchmarks and pruning ratios demonstrate that VanishGS consistently preserves higher rendering quality. Moreover, VanishGS can be integrated into existing post compression pipelines, further demonstrating its generality as a plug-and-play pruning strategy.