Denoising-GS: Gaussian Splatting with Spatial-aware Denoising

Recent advances in 3D Gaussian Splatting (3DGS) have ach-ieved significant success in high-fidelity Novel View Synthesis (NVS),yet the optimization process still introduces noisy Gaussian primitivesdue to the sparse and incomplete initialization from Structure-from-Motion (SfM) point clouds. Most existing methods focus only on adjust-ing the positions of primitives during optimization, while ignoring theunderlying spatial structure. To this end, we introduce a new perspec-tive by formulating the optimization of 3DGS as a primitive denoisingprocess and propose Denoising-GS, a spatial-aware denoising frame-work for Gaussian primitives by taking both the positions and spatialstructure into consideration. Specifically, we design an optimizer thatpreserves the spatial optimization flow of primitives, facilitating coher-ent and directed denoising rather than random perturbations. Buildingupon this, the Spatial Gradient-based Denoising strategy jointlyconsiders the spatial supports of primitives to ensure gradient-consistentupdates. Furthermore, the Uncertainty-based Denoising module es-timates primitive-wise uncertainty to prune redundant or noisy primi-tives, while the Spatial Coherence Refinement strategy selectivelysplits primitives in sparse regions to maintain structural completeness.Experiments conducted on three benchmark datasets demonstrate thatDenoising-GS consistently enhances NVS fidelity while maintaining rep-resentation compactness, achieving state-of-the-art performance acrossall benchmarks. Source code and models will be made publicly available.(a) (b) (c) (d)Point Cloud Spatial Gradient- Uncertainty-basedPosition DenoisingDenoising based Denoising Denoising↓ optimize and denoise ↓ ↓calculate uncertainty and prune↓Fig. 1: Illustrative comparison of different denoising methods: (a) Point cloud denois-ing, which moves noisy points toward the GT surface; (b) Position denoising of Gaus-sian primitives, which adjusts the mean positions of noisy primitives toward the GTsurface; (c) Spatial gradient-based denoising of Gaussian primitives, which optimizesboth the mean positions and the spatial structure of primitives based on gradients; (d)Uncertainty-based denoising of Gaussian primitives, which estimates the uncertaintyof each primitive and prunes those with high uncertainty.