Beyond Coverage: Single-View Signals for Post-Hoc Uncertainty Estimation in Gaussian Splatting

Post-hoc uncertainty estimation for Gaussian Splatting aims to predict rendering errors without modifying a trained scene representation. Existing approaches largely rely on multi-view coverage statistics, requiring training-view rendering, ground-truth comparison, and back-projection onto Gaussian primitives. We revisit where predictive uncertainty signals originate and show that much of the signal is already present in a single rendering at the novel pose. Four local appearance statistics extracted from the colour buffer are as predictive as the strongest coverage-based features, while a simple gradient-magnitude map remains competitive without any learned regressor. These appearance cues and geometry statistics are largely complementary: a representation combining both, together with scale-invariant statistics from the rendered depth buffer, improves Pearson correlation by 23%, Spearman correlation by 17%, and reduces AUSE by 14% over the strongest post-hoc baseline across 23 scenes from four datasets. This scale-invariant representation further enables a single estimator trained across scenes to transfer to unseen scenes without target-scene fitting. We also identify monotone view-dependent feature drift as a source of evaluation bias, and show that an evidential head on within-view rank representations, which removes this drift by construction, adds calibrated predictive intervals at no cost in ranking accuracy. These findings suggest that reliable post-hoc uncertainty in Gaussian Splatting depends not only on scene observation history but also on signals intrinsic to the rendered view itself.