Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection
The deployment of zero-shot anomaly detection (AD) in embodied industrial inspection is severely bottlenecked by its reliance on passive, fixed-viewpoint 2D imagery. Such formulations inherently fail to accommodate the active, dynamic observations required in real-world environments. To break this limitation, we introduce Real-to-Twin Anomaly Detection, a novel task that evaluates physical observations directly against geometrically matched CAD Digital Twins. To tackle this new task, we propose AVATAR, a framework designed to learn robust semantic alignment between Real and Digital Twins. By bridging benign Sim2Real domain gaps using only defect-free pairs, AVATAR effectively transforms C...
| Authors | Jiaxuan Liu, Yunkang Cao, Yufeng Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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