Compression-aware edge densification for compact 3d gaussian splatting

Improving novel-view synthesis under a fixed storage budget requires deciding not only which Gaussians to retain, but also how to create them. While compression pipelines emphasize pruning, the representation built before pruning offers another opportunity to improve compact reconstruction. We introduce Compression-Aware Edge Densification (CAED), which controls early Gaussian growth for a fixed-capacity output. CAED adapts edge-guided selection and long- axis splitting from ImprovedGS, limits growth with a square-root budget, and uses the reconstruction-aware pruning and Difference-of-Gaussians refinement of PWRS to produce a compact model. With exactly the same final Gaussian count and PLY size as PWRS in every scene, CAED improves mean PSNR by 0.2384 dB and SSIM by 0.0045, and reduces LPIPS by 0.0049 across 13 scenes. LPIPS improves in 12 of the 13 scenes. Three-seed experiments on five scenes further support the overall quality gains, with an observed reduction of approximately 21% in training-command wall-clock time within the same framework. These results show that designing early densification for a compact output can improve reconstruction without increasing final model capacity.