AI RESEARCH
Breaking the Rigid Prior: Towards Articulated 3D Anomaly Detection
arXiv CS.CV
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ArXi:2604.26868v1 Announce Type: new Existing 3D anomaly detection methods are built on a rigid prior: normal geometry is pose-invariant and can be canonicalized through registration or alignment. This prior does not hold for articulated objects with hinge or sliding joints, where valid pose changes induce structured geometric variations that cannot be collapsed to a single canonical template, causing pose-induced deformations to be misidentified as anomalies while true structural defects are obscured. No existing benchmark addresses this challenge. We.