Improving Sparse-View 3DGS Generalization via Flat Minima Optimization

Recent advances in neural rendering have established 3DGaussian Splatting (3DGS) as a highly efficient representation for novelview synthesis, enabling fast training and real-time rendering with strongfidelity. However, when supervision is limited to sparse input views,3DGS tends to overfit to the observed images and generalize poorlyto unseen viewpoints. We address this challenge from the perspectiveof flat minima (FM) optimization, which seeks solutions that remainstable under small parameter perturbations. Viewing Gaussian parame-ters as trainable weights, we adapt FM principles to the geometric anddynamic nature of 3DGS with a lightweight training framework. Ourmethod regularizes optimization with controlled Gaussian perturbationsthat account for each Gaussian’s anisotropy and the training progress,preserving fine details while improving robustness to sparse-view overfit-ting. To further stabilize this flat minima optimization process, we intro-duce periodic reinitialization, which temporarily returns non-positionalparameters to their initial states for a short window. Together, thesetechniques integrate seamlessly into existing 3DGS pipelines without ar-chitectural changes. Experiments on LLFF and Mip-NeRF360 datasetsdemonstrate improved quantitative metrics and perceptual quality un-der sparse-view supervision, producing reconstructions that are sharper,more stable, and better generalized to novel viewpoints.