AI RESEARCH
PiCo: Active Manifold Canonicalization for Robust Robotic Visual Anomaly Detection
arXiv CS.CV
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ArXi:2603.23122v1 Announce Type: new Industrial deployment of robotic visual anomaly detection (VAD) is fundamentally constrained by passive perception under diverse 6-DoF pose configurations and unstable operating conditions such as illumination changes and shadows, where intrinsic semantic anomalies and physical disturbances coexist and interact. To overcome these limitations, a paradigm shift from passive feature learning to Active Canonicalization is proposed. PiCo (Pose-in-Condition Canonicalization) is.