Where Fixes Really Help: Cross-Fitted Repair-Model Supervision for 3DGS

Sparse-view 3D Gaussian Splatting (3DGS) is severely underconstrained away from observed cameras, often producing unstable geometry and appearance. Pretrained repair models can enrich supervision by correcting rendered novel views, yet their outputs may also introduce scene-inconsistent content that 3DGS absorbs into persistent artifacts. The resulting challenge is to determine which repaired pixels provide reliable supervision. We introduce FixTrust-GS, a framework that estimates pixel-wise repair reliability from the captured scene. FixTrust-GS constructs cross-fitted auxiliary reconstructions using only captured images and evaluates whether each repaired pixel agrees with their predictions better than the corresponding initial render. This relative agreement improvement yields dense weights for continued 3DGS optimization, retaining supported repair content while suppressing inconsistent edits. The framework requires only rendered–repaired image pairs and works with pretrained repair models without modifying their parameters. Experiments across three datasets, two distinct repair models, and three sparse-view budgets show that FixTrust-GS improves reconstruction fidelity over uniform whole-image weighting while often using fewer Gaussians in the final model. Code: https://anonymous.4open.science/r/FixTrust-GS-1D4B.