Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable success in high-fidelity Novel View Synthesis (NVS), yet the optimization process inevitably introduces noisy Gaussian primitives due to the sparse and incomplete initialization from Structure-from-Motion (SfM) point clouds. Most existing methods focus solely on adjusting the positions of primitives during optimization, while neglecting the underlying spatial structure. To this end, we introduce a new perspective by formulating the optimization of 3DGS as a primitive denoising process and propose Denoising-GS, a spatial-aware denoising framework for Gaussian primitives by taking both the positions and spatial structure into consideration. Specifically, we design an optimizer that preserves the spatial optimization flow of primitives, facilitating coherent and directed denoising rather than random perturbations. Building upon this, the Spatial Gradient-based Denoising strategy jointly considers the spatial supports of primitives to ensure gradient-consistent updates. Furthermore, the Uncertainty-based Denoising module estimates primitive-wise uncertainty to prune redundant or noisy primitives, while the Spatial Coherence Refinement strategy selectively splits primitives in sparse regions to maintain structural completeness. Experiments conducted on three benchmark datasets demonstrate that Denoising-GS consistently enhances NVS fidelity while maintaining representation compactness, achieving state-of-the-art performance across all benchmarks. Source code and models will be made publicly available.

翻译：近期三维高斯溅射（3DGS）在高质量新视角合成（NVS）领域取得了显著成功，但由于运动恢复结构（SfM）点云初始化的稀疏性和不完整性，优化过程不可避免地引入了噪声高斯基元。现有方法大多仅关注优化过程中基元位置的调整，而忽视了其潜在空间结构。为此，我们提出将3DGS优化问题重新定义为基元去噪过程的新视角，并设计了Denoising-GS——一种兼顾位置与空间结构的空间感知高斯基元去噪框架。具体而言，我们构建了一个保持基元空间优化流场的优化器，从而促进连贯定向的去噪而非随机扰动。在此基础上，基于空间梯度的去噪策略通过联合考虑基元的空间支撑域确保梯度一致性更新。此外，基于不确定性的去噪模块通过估计基元级别的不确定性来剔除冗余或噪声基元，而空间一致性精炼策略则选择性地在稀疏区域分裂基元以维持结构完整性。在三个基准数据集上的实验表明，Denoising-GS在保持表示紧凑性的同时持续提升NVS保真度，在所有基准测试中均实现了最先进的性能。源代码与模型将公开发布。