← 研踪 PaperTrailarXiv:2610.35806 · v1
Does VGGT Know What It Does Not Know?
首次提交 2026-10-27 01:37
版本更新 2026-10-27 01:37
雷达观测 2026-10-29 00:35
摘要
Feed-forward 3D reconstruction models such as VGGT regress cameras and geometry in a single forward pass, but they report no uncertainty for the camera poses they predict: an incorrect relative pose looks no different from a correct one. We ask whether the frozen model nonetheless knows when its pose is wrong. On HardMatch, a benchmark of 1,000 deliberately difficult image pairs on which VGGT fails on 35% of pairs, a linear probe on VGGT's frozen features predicts pose failure on held-out categories with ROC-AUC 93.5%, compared with 79.7% for VGGT's own point-map confidence and 77.7% for a classifier trained on all of VGGT's outputs. The probe catches failures VGGT is confident about, lets a downstream system abstain on the pairs it distrusts, and transfers without retraining to rotation failures on PhotoTourism and ScanNet-1500, beyond what co-visibility explains. This knowledge is absent from the per-image encoder: it is computed partway through the layers that fuse the two views, and the same holds for a second model, $π^3$. In essence, we find that VGGT knows more about its own failures than it reports.
作者备注：9 pages, 6 figures, 7 tables
观测到的版本
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v1 · 2026-10-27 01:37 · 最新观测版本
Does VGGT Know What It Does Not Know?
Feed-forward 3D reconstruction models such as VGGT regress cameras and geometry in a single forward pass, but they report no uncertainty for the camera poses they predict: an incorrect relative pose looks no different from a correct one. We ask whether the frozen model nonetheless knows when its pose is wrong. On HardMatch, a benchmark of 1,000 deliberately difficult image pairs on which VGGT fails on 35% of pairs, a linear probe on VGGT's frozen features predicts pose failure on held-out categories with ROC-AUC 93.5%, compared with 79.7% for VGGT's own point-map confidence and 77.7% for a classifier trained on all of VGGT's outputs. The probe catches failures VGGT is confident about, lets a downstream system abstain on the pairs it distrusts, and transfers without retraining to rotation failures on PhotoTourism and ScanNet-1500, beyond what co-visibility explains. This knowledge is absent from the per-image encoder: it is computed partway through the layers that fuse the two views, and the same holds for a second model, $π^3$. In essence, we find that VGGT knows more about its own failures than it reports.
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