SkipGS: Post-Densification Backward Skipping for Efficient 3DGS Training

3D Gaussian Splatting (3DGS) achieves real-time novel-viewsynthesis by optimizing millions of anisotropic Gaussians, yet its trainingremains expensive, with the backward pass dominating runtime in thepost-densification refinement phase. We observe substantial update re-dundancy in this phase: many sampled views have near-plateaued lossesand provide diminishing gradient benefits, but standard training stillruns full backpropagation. We propose SkipGS with a novel view-adaptivebackward gating mechanism for efficient post-densification training. SkipGSalways performs the forward pass to update per-view loss statistics, andselectively skips backward passes when the sampled view’s loss is consis-tent with its recent per-view baseline, while enforcing a minimum back-ward budget for stable optimization. On Mip-NeRF 360, compared to3DGS, SkipGS reduces end-to-end training time by 23.1%, driven by a42.0% reduction in post-densification time, with comparable reconstruc-tion quality. Because it only changes when to backpropagate withoutmodifying the renderer, representation, or loss, SkipGS is plug-and-playand compatible with other complementary efficiency strategies, enablingadditive speedups. Code is available at https://github.com/ASU-ESIC-FAN-Lab/SkipGS.