Confidence Is Not Coverage: Budgeted Confidence Ranking Can Fail Under Fixed Reconstruction Budgets

Feed-forward multi-view reconstruction models produce dense 3D predictions with per-point confidence, but downstream representations typically retain only a fixed number of points or primitives. A simple strategy is therefore to rank predictions by confidence before sampling. We show that this seemingly natural use of confidence can be mismatched to fixed-budget reconstruction: because confidence scores points independently, ranking can remove all candidates from parts of the scene before a spatial sampler is ever applied. We isolate the effect of confidence ranking with a matched pool test that compares confidence-ranked and uniformly sampled candidate pools of identical size under the same sampler, budget, and initialization. We also derive a coverage ceiling that upper-bounds the reconstruction quality attainable by any sampler that selects from a given pool without moving its points, allowing irreversible support loss to be detected from reference geometry before sampling. Across multiple reconstruction models and benchmarks, confidence-ranked pools can provide substantially less scene support than equal-sized random pools. Mixing ranked and random candidates restores most of the lost performance. Our results show that confidence remains useful for rejecting unreliable predictions, but is not by itself a suitable allocation rule for a tight scene-level budget.