From Uncertainty to Stability and Fidelity: Guiding Sparse-View 3D Gaussian Splatting with Fisher Information

3D Gaussian Splatting (3DGS) achieves high-quality novel view synthesis but requires dense input views and overfits in sparse-view settings, producing noticeable artifacts and degraded rendering quality. Previous methods mitigate this issue by introducing additional geometric priors or regularization techniques (e.g. Dropout). However, these methods lack principled guidance: prior-based augmentation randomly samples novel viewpoints, while Dropout-based regularization randomly removes Gaussians, which introduces uncertainty and instability. In this paper, we propose a Fisher Information-guided method that quantitatively guides both strategies. First, Fisher Information-guided Stereo Augmentation actively select most informative supporting views and use depth priors to curate reliable pseudo ground truths, which improves stability and rendering fidelity. Second, Uncertainty-aware regularization measures the uncertainty of each Gaussian using Fisher Information, and adaptively adjusts the removal probability, leading to more stable and effective regularization. With these two components, our method effectively mitigates overfitting and improves the stability of optimization in sparse-view 3DGS, resulting in superior rendering fidelity. Extensive experiments on sparse-view novel view synthesis show that our method achieves state-of-the-art rendering quality.