Subspace Representations for Synthetic-to-Real Transfer in 3-D Surface Inspection

Abstract:This paper studies what subspace representations contribute to synthetic-to-real transfer in 3-D surface inspection, where models learn from synthetic defects and the only real data before deployment are scans of good parts. We use three classical constructions as instruments: the nearest-subspace classifier, one-class template subspaces and the generalized difference subspace (GDS). On 1,197 MVTec 3D-AD scans without target annotation we find that every decision rule ranks real defects at a similar window AUC, that image-level detection stays at chance, and that no rule keeps its in-domain threshold. We show that the margin of a nearest-subspace rule moves by at most the part of the feature shift outside its class subspaces, and that this leakage, measured from unlabeled good scans, bounds the displacement observed on unseen scans. On 597 Real3D-AD objects, a one-class subspace of registered templates evaluated at every location on frozen features reaches point AUROC 0.957 on pre-registered scans and a category-mean object AUROC of 78 to 81 with no training on the benchmark. Our results suggest that a subspace helps under shift in proportion to the shift it spans, and that in reference-based inspection the reference and its registration matter more than the subspace.

16 pages, 2 figures, 2 tables. Accepted to the ACCV 2026 Workshop on Subspaces in the Era of Foundation Models. Website: this https URL

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2611.02744 [cs.CV]

(or arXiv:2611.02744v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2611.02744 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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