关键词: 稀疏视角, 高斯溅射, 生成先验, 掩膜引导补全修复扩散模型, 渐进式融合

Abstract: Gaussian splatting achieves a favorable balance between efficiency and reconstruction quality.However,under sparse-view conditions,insufficient observations lead to under-constrained geometry and appearance,resulting in structural incompleteness.Consequently,novel view synthesis often suffers from missing content in unobserved regions and rendering artifacts,degra-ding visual realism and multi-view consistency.To address these issues,this paper proposes a generative prior-guided optimization framework for sparse-view Gaussian splatting reconstruction.Specifically,this paper builds a mask-guided restoration diffusion(MRD) model based on the pretrained degradation removal diffusion model DiFix,and further fine-tunes it on establishedtraining dataset.MRD introduces a lightweight mask prediction branch(Mask Head) to localize restoration regions primarily correspon-ding to missing areas,while also responding to certain rendering artifacts.During training,a mask-guided selective noise injection strategy is adopted,where random noise is mainly applied to the missing regions to enhance completion performance,while preserving the ability to correct rendering artifacts.The generated results from MRD are then periodically incorporated into the Gaussian splatting training as pseudo-observations,and a progressive fusion strategy is employed to gradually expand the viewpoint span of pseudo-observations in a controlled manner,thereby strengthening constraints in unobserved regions and improving multi-view consistency.In addition,a region-weighted constraint is introduced to reduce the impact of uncertainty in pseudo-observations on the optimization process.Experiments on the DL3DV and Mip-NeRF 360 datasets demonstrate that the proposed method is compatible with both 2DGS and 3DGS pipelines and achieves superior performance across multiple evaluation metrics.Qualitative results further show that the proposed method produces more complete and stable reconstructions under sparse-view settings,significantly improving visual quality and multi-view consistency in novel view synthesis.

Key words: Sparse-view, Gaussian splatting, Generative prior, Mask-guided restoration diffusion model, Progressive fusion