AF3AD is a modular synthesis framework using center-conditioned parametric deformations in local PCA frames to create diverse pseudo-anomalies, improving unsupervised 3D anomaly detection on AnomalyShapeNet and Real3D-AD.
Dfr: Deep feature reconstruction for unsupervised anomaly segmentation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3verdicts
UNVERDICTED 3representative citing papers
Hypergraph model on DINOv2 tokens raises logical anomaly AUROC to 0.9279 on MVTec LOCO breakfast-box data by scoring an information quotient across local, relational, and hyperedge evidence.
A new framework learns low-dimensional subspaces from nominal samples and reconstructs target deep embeddings via self-expressive linear combinations to localize anomalies, claiming SOTA on three benchmarks.
citing papers explorer
-
Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection
AF3AD is a modular synthesis framework using center-conditioned parametric deformations in local PCA frames to create diverse pseudo-anomalies, improving unsupervised 3D anomaly detection on AnomalyShapeNet and Real3D-AD.
-
Hypergraph Normal World Models for Logical Visual Anomaly Detection
Hypergraph model on DINOv2 tokens raises logical anomaly AUROC to 0.9279 on MVTec LOCO breakfast-box data by scoring an information quotient across local, relational, and hyperedge evidence.
-
Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization
A new framework learns low-dimensional subspaces from nominal samples and reconstructs target deep embeddings via self-expressive linear combinations to localize anomalies, claiming SOTA on three benchmarks.