CMDS-AD decouples low-frequency normals via diffusion estimator from high-frequency defects in real RGB data using dual streams, a hierarchical mapper, and multiplicative scoring to achieve SOTA 1-shot anomaly detection gains on MVTec 3D-AD and EyeCandies.
The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization
11 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 11representative citing papers
Real-IAD-MVN supplies multi-view normal vector data and a reconstruction baseline that outperforms prior multimodal methods for geometric industrial anomaly detection.
Align3D-AD improves zero-shot 3D anomaly detection by cross-modal feature alignment from RGB guidance and dual-prompt contrastive alignment to capture complementary semantics.
MMR-AD is a new benchmark dataset showing that current generalist MLLMs lag industrial needs for anomaly detection, with Anomaly-R1 delivering better results through reasoning and RL.
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.
TopoTTA integrates persistent homology into test-time adaptation to derive topological pseudo-labels from anomaly maps, improving segmentation by an average 15% F1 on six benchmarks while generalizing across 2D and 3D data.
CoGeoAD fuses color and geometric features via Data-Driven Multi-View Attention and Multi-Stage Color-Geometric Fusion in a CLIP backbone to reach SOTA zero-shot 3D anomaly detection on MVTec3D-AD and Eyecandies.
VT-3DAD fuses visual deviation from few-shot references and semantic deviation from textual normal space to achieve SOTA cross-category 3D anomaly detection on ShapeNetPart.
AAND is a two-stage anomaly detection method that advances a pre-trained teacher via residual anomaly amplification and applies hard knowledge distillation in reverse distillation to achieve SOTA results on MVTecAD, VisA, and MVTec3D-RGB.
Proposes MODIAD framework with MIS scheduling solved via SMG algorithm and REC-LoRA adaptation for efficient multimodal online distributed industrial anomaly detection, reporting superior performance on MVTec 3D-AD and Eyecandies datasets.
ZSG-IAD is a zero-shot multimodal system that uses language-guided two-hop grounding and rule-based reinforcement learning to produce anomaly masks and explainable reports from industrial sensor data.
citing papers explorer
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CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection
CMDS-AD decouples low-frequency normals via diffusion estimator from high-frequency defects in real RGB data using dual streams, a hierarchical mapper, and multiplicative scoring to achieve SOTA 1-shot anomaly detection gains on MVTec 3D-AD and EyeCandies.
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Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection
Real-IAD-MVN supplies multi-view normal vector data and a reconstruction baseline that outperforms prior multimodal methods for geometric industrial anomaly detection.
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Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection
Align3D-AD improves zero-shot 3D anomaly detection by cross-modal feature alignment from RGB guidance and dual-prompt contrastive alignment to capture complementary semantics.
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MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models
MMR-AD is a new benchmark dataset showing that current generalist MLLMs lag industrial needs for anomaly detection, with Anomaly-R1 delivering better results through reasoning and RL.
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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.
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Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation
TopoTTA integrates persistent homology into test-time adaptation to derive topological pseudo-labels from anomaly maps, improving segmentation by an average 15% F1 on six benchmarks while generalizing across 2D and 3D data.
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CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection
CoGeoAD fuses color and geometric features via Data-Driven Multi-View Attention and Multi-Stage Color-Geometric Fusion in a CLIP backbone to reach SOTA zero-shot 3D anomaly detection on MVTec3D-AD and Eyecandies.
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VT-3DAD: Cross-Category 3D Anomaly Detection via Visual-Text Normal Space Alignment
VT-3DAD fuses visual deviation from few-shot references and semantic deviation from textual normal space to achieve SOTA cross-category 3D anomaly detection on ShapeNetPart.
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Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection
AAND is a two-stage anomaly detection method that advances a pre-trained teacher via residual anomaly amplification and applies hard knowledge distillation in reverse distillation to achieve SOTA results on MVTecAD, VisA, and MVTec3D-RGB.
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Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
Proposes MODIAD framework with MIS scheduling solved via SMG algorithm and REC-LoRA adaptation for efficient multimodal online distributed industrial anomaly detection, reporting superior performance on MVTec 3D-AD and Eyecandies datasets.
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ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection
ZSG-IAD is a zero-shot multimodal system that uses language-guided two-hop grounding and rule-based reinforcement learning to produce anomaly masks and explainable reports from industrial sensor data.