SkipGS: Post-Densification Backward Skipping for Efficient 3DGS Training
3D Gaussian Splatting (3DGS) achieves real-time novel-view synthesis by optimizing millions of anisotropic Gaussians, yet its training remains expensive, with the backward pass dominating runtime in the post-densification refinement phase. We observe substantial update redundancy in this phase: many sampled views have near-plateaued losses and provide diminishing gradient benefits, but standard training still runs full backpropagation. We propose SkipGS with a novel view-adaptive backward gating mechanism for efficient post-densification training. SkipGS always performs the forward pass to update per-view loss statistics, and selectively skips backward passes when the sampled view's loss ...
| Authors | Jingxing Li, Yongjae Lee, Deliang Fan |
|---|---|
| Primary category | cs.CV |
| Published | 9 March 2026 |
| Last updated at source | 1 July 2026 |
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