Per-View Gaussian Predictions Enable Training-Free Distractor Filtering in Feed-Forward 3DGS

Feed-forward 3D Gaussian Splatting reconstructs an explicit Gaussian representation from multiple input images, making 3D reconstruction increasingly accessible for casual captures. However, such captures frequently contain transient content that is inconsistent across the input views. Such content can persist in the reconstructed Gaussian representation despite conflicting with observations from other views. As a result, it may produce blurred, duplicated, or floating artifacts in novel views. We introduce a training-free filtering procedure that exploits the association between predicted Gaussians and their corresponding input views. Self-view filtering compares each input with a rendering that excludes its associated Gaussians, exposing content unsupported by the remaining Gaussians. Cross-view filtering measures how those Gaussians interfere with reconstruction of other input views. Gaussians selected by either filtering path are removed together. The procedure operates on a single frozen prediction without retraining or scene-specific optimization. Evaluations across recent feed-forward 3DGS models on novel view synthesis benchmarks with distractors in the input images show improved rendering quality with varying numbers of input views. On clean inputs, the original reconstructions are largely preserved.