VDGS: Improving Fidelity in Fast 3D Gaussian Splatting Training via Virtual Densification

Fast 3D Gaussian Splatting training must improve fidelity with limited densification. Existing candidate criteria often aggregate gradients or errors at the parent level. Visible-view averages do not directly capture cross-view frequency, while parent aggregation obscures demand across the sides of a prescribed split. We propose VDGS, based on Virtual Densification (VD), to rank candidate splits before creating children. Parent gradient statistics first nominate candidates; the prescribed split geometry then partitions their pixelwise backward signals into two virtual child domains. VDGS combines two-sided activity and net-gradient direction differences, accumulates this evidence over a selection window, and ranks splits for submission. It reuses the regular backward pass without temporary children or trial training. In a common low-budget framework, full VDGS improves mean PSNR over direct AbsGS growth by approximately 0.45 dB on nine Mip-NeRF 360 scenes, while also improving SSIM and LPIPS.