A Calibration Audit of Confidence in Feed-Forward 3D Reconstruction
Quick summary
arXiv:2608.29705v2 Announce Type: replace-cross Abstract: Feed-forward 3D reconstruction models emit a per-pixel confidence that downstream systems read as a reliability signal. It is trained as a loss weight, not as an uncertainty magnitude, and whether it can be used as an error prediction has not been measured. We audit seven released backbones on thirteen datasets and score the confidence on four properties, how well it ranks error, whether its level is right on average, whether it holds across the confidence range, and whether its intervals cover the truth. The confidence ranks error well
Key takeaways
- arXiv:2608.29705v2 Announce Type: replace-cross Abstract: Feed-forward 3D reconstruction models emit a per-pixel confidence that downstream systems read as a reliability signal.
- It is trained as a loss weight, not as an uncertainty magnitude, and whether it can be used as an error prediction has not been measured.
- We audit seven released backbones on thirteen datasets and score the confidence on four properties, how well it ranks error, whether its level is right on average, whether it holds across the confidence range, and whether its intervals cover the truth.
Why it matters
The importance of “A Calibration Audit of Confidence in Feed-Forward 3D Reconstruction” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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