Breaking the Rigid Prior: Towards Articulated 3D Anomaly Detection
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 introduce ArtiAD, the first large-scale benchmark for articulated 3D anomaly detection, comprising 15,229 point clouds across 39 object categories with dense joint-angle varia...
| Authors | Jinye Gan, Bozhong Zheng, Xiaohao Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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