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  1. Home
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  3. A Semantically Disentangled Unified Model for Multi-category
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A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection

Fresh1d ago
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Viability
0.0/10

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Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: pending

Distribution: unknown

Source paper: A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection

PDF: https://arxiv.org/pdf/2603.25159v1

First buyer signal: unknown

Distribution channel: unknown

Starting…

Dimensions overall score 7.0

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Keep exploring

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Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detection
Score 7.0stable
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Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects
Score 7.0stable
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Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
Score 7.0stable
Prior Work
UniDA3D: A Unified Domain-Adaptive Framework for Multi-View 3D Object Detection
Score 7.0stable
Prior Work
Group3D: MLLM-Driven Semantic Grouping for Open-Vocabulary 3D Object Detection
Score 7.0stable
Prior Work
GeoGuide: Hierarchical Geometric Guidance for Open-Vocabulary 3D Semantic Segmentation
Score 7.0stable
Higher Viability
AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models
Score 8.0up
Competing Approach
Modulate-and-Map: Crossmodal Feature Mapping with Cross-View Modulation for 3D Anomaly Detection
Score 7.0stable

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