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Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models
Shengzhe Chen, Mehrdad Moradi, Kamran Paynabar +1
We propose Flow Mismatching, an unsupervised anomaly detection method that deliberately avoids reconstruction-based paradigms. Instead, we treat flow matching as geometric dynamics…
Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach
Mariafrancesca Patalano, Giovanna Capizzi, Kamran Paynabar
Advanced manufacturing technologies allow for the production of intricate parts featuring high shape complexity and spatially-varying material composition. Data fusion of point clo…
Registration-Free Monitoring of Unstructured Point Cloud Data via Intrinsic Geometrical Properties
Mariafrancesca Patalano, Giovanna Capizzi, Kamran Paynabar
Modern sensing technologies have enabled the collection of unstructured point cloud data (PCD) of varying sizes, which are used to monitor the geometric accuracy of 3D objects. PCD…
Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models
Mehrdad Moradi, Marco Grasso, Bianca Maria Colosimo +1
Generative models have demonstrated significant success in anomaly detection and segmentation over the past decade. Recently, diffusion models have emerged as a powerful alternativ…
A Single Image Is All You Need: Zero-Shot Anomaly Localization Without Training Data
Mehrdad Moradi, Shengzhe Chen, Hao Yan +1
Anomaly detection in images is typically addressed by learning from collections of training data or relying on reference samples. In many real-world scenarios, however, such traini…
RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentation
Mehrdad Moradi, Kamran Paynabar
Recent advancements in diffusion models have demonstrated significant success in unsupervised anomaly segmentation. For anomaly segmentation, these models are first trained on norm…