能動的実物-デジタルツイン検査に向けて: ゼロショット異常検出の新パラダイム
Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection
固定視点の2D画像に依存する従来のゼロショット異常検出の限界を打破するため、実物の観測をCADデジタルツインと直接照合する新しいタスクを提案し、欠陥のないペアのみで学習するフレームワークAVATARを開発した。
著者: Jiaxuan Liu, Yunkang Cao, Yufeng Chen, Chunyang Li, Yuhuan Du, Hui Zhang
分類: cs.CV
原文アブストラクト
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 CAD priors into dynamic, anomaly-free references. This elegant formulation enables the model to localize diverse anomalies in a zero-shot manner as unalignable deviations, eliminating the need for defect annotations. Extensive experiments demonstrate that AVATAR substantially outperforms adapted state-of-the-art baselines, exhibiting exceptional robustness to severe viewpoint variations. The code and dataset will be made publicly available.