PriSplat: Propagating Reliable Multi-view Information for Distractor-Free 3DGS

Applying 3D Gaussian Splatting (3DGS) to uncontrolled, in-the-wild environments remains challenging due to transient distractors.Existing approaches typically mask these distractors and suppress theirlosses during training. However, this zero-masking strategy leaves suchregions unsupervised, leading to per-view overfitting and causing severefloaters in novel views. To resolve this, it is essential to re-establish densemulti-view constraints by recovering the missing background informa-tion. In light of this, we propose PriSplat, a novel framework designed topropagate reliable multi-view information to restore these missing regionswith high geometric integrity. Specifically, we repurpose a large-scaleview synthesis prior into a 3D-aware inpainting engine, adapted throughmask-aware fast-weight updates to prevent distractor leakage into thescene memory. To ensure the fidelity of this restoration, we introducea geometry-aware support view selection algorithm based on informa-tion density and spatio-angular constraints. Ultimately, these synergisticadvancements yield 3D-consistent pseudo-ground truth from masked re-gions, establishing the dense supervision necessary to eliminate artifacts.Extensive experiments show that our method outperforms state-of-the-art baselines in both synthesis quality and multi-view consistency. Thecode is available at https://github.com/yun-seo/PriSplat.git.