RogSplat: Robust Gaussian Splatting via Generative Priors

3D Gaussian Splatting (3DGS) has recently emerged as an efficient representation for high-quality 3D reconstruction and rendering. Despite its superior rendering quality and speed, 3DGS heavily relies on the assumption of geometric consistency among input images. In real-world scenarios, violations of this assumption—such as occlusions, dynamic objects, or camera blur—often lead to reconstruction artifacts and rendering inaccuracies. To address these challenges, we introduce RogSplat, a robust framework that leverages generative models to enhance the reliability of 3DGS. Specifically, RogSplat identifies and rectifies occluded regions during the optimization of unstructured scenes. Outlier regions are first detected using our proposed fused features and then accurately inpainted by the proposed RF-Refiner, ensuring reliable reconstruction of occluded areas while preserving the integrity of visible regions. Extensive experiments demonstrate that RogSplat achieves state-of-the-art reconstruction quality on the RobustNeRF and NeRF-on-the-go datasets, significantly outperforming existing methods in challenging real-world scenarios involving dynamic objects.