用数字孪生匹配真实工业检测，零样本定位缺陷
Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection
- 构建真实与数字孪生的语义对齐，无需缺陷标注
- 在视角剧烈变化下仍能准确检测多种异常
- 适合工业质检中无缺陷数据的场景
将零样本异常检测应用于具身工业检测时，受限于依赖静态二维图像的被动观测模式，难以适应真实环境中的主动动态观察需求。为此，我们提出「真实到数字孪生异常检测」这一新任务，直接将物理观测与几何匹配的CAD数字孪生进行对比。为解决该任务，我们提出AVATAR框架，通过仅使用无缺陷样本对齐真实与数字孪生间的良性域差异，将CAD先验转化为动态、无异常的参考基准。该设计使模型以不可对齐的偏差形式实现零样本异常定位，无需缺陷标注。大量实验表明，AVATAR显著优于适配的现有先进基线，在严重视角变化下仍具备出色鲁棒性。代码与数据集将公开。
原文摘要 · Abstract (English)
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.
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