5 papers
MATCH: Flow Matching for Multi-View Anomaly Detection
Mathis Kruse, Melissa Schween, Bodo Rosenhahn
Detecting anomalies in industrial objects is an important topic for increasing production efficiency. More complex objects often require the analysis of several view points, which…
BUSSARD: Normalizing Flows for Bijective Universal Scene-Specific Anomalous Relationship Detection
Melissa Schween, Mathis Kruse, Bodo Rosenhahn
We propose Bijective Universal Scene-Specific Anomalous Relationship Detection (BUSSARD), a normalizing flow-based model for detecting anomalous relations in scene graphs, generate…
Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection
Mathis Kruse, Bodo Rosenhahn
With more well-performing anomaly detection methods proposed, many of the single-view tasks have been solved to a relatively good degree. However, real-world production scenarios o…
AutoML for Multi-Class Anomaly Compensation of Sensor Drift
Melanie Schaller, Mathis Kruse, Antonio Ortega +2
Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as…
SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection
Mathis Kruse, Marco Rudolph, Dominik Woiwode +1
Detecting anomalies in images has become a well-explored problem in both academia and industry. State-of-the-art algorithms are able to detect defects in increasingly difficult set…