mlsanityData to 14 Oct 2026

SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection

arXiv 2604.05632 ↗First posted 7 Apr 2026cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Multi-view anomaly detection aims to identify surface defects on complex objects using observations captured from multiple viewpoints. However, existing unsupervised methods often suffer from feature inconsistency arising from viewpoint variations and modality discrepancies. To address these challenges, we propose a Semantic and Geometric Alignment Network (SGANet), a unified framework for multimodal multi-view anomaly detection that effectively combines semantic and geometric alignment to learn physically coherent feature representations across viewpoints and modalities. SGANet consists of three key components. The Selective Cross-view Feature Refinement Module (SCFRM) selectively aggregates informative patch features from adjacent views to enhance cross-view feature interaction. The Semantic-Structural Patch Alignment (SSPA) enforces semantic alignment across modalities while maintaining structural consistency under viewpoint transformations. The Multi-View Geometric Alignment (MVGA) further aligns geometrically corresponding patches across viewpoints. By jointly modeling feature interaction, semantic and structural consistency, and global geometric correspondence, SGANet effectively enhances anomaly detection performance in multimodal multi-view settings. Extensive experiments on the SiM3D and Eyecandies datasets demonstrate that SGANet achieves state-of-the-art performance in both anomaly detection and localization, validating its effectiveness in realistic industrial scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 7 Apr 2026.

Builds on

31 of 48 references.

References · 31

  1. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  4. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  5. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  6. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  7. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  8. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
  9. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 117
  10. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 111
  11. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  12. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 90
19 more
  1. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 75
  2. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  3. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  4. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  5. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 42
  6. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  7. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  8. 2024Self-supervised Feature Adaptation for 3D Industrial Anomaly Detectioncited by 16
  9. 2024Learning Multi-view Anomaly Detection with Efficient Adaptive Selectioncited by 9
  10. 2024Dual-path Frequency Discriminators for Few-shot Anomaly Detectioncited by 7
  11. 2025G2SF-MIAD: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detectioncited by 7
  12. 2025Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memorycited by 6
  13. 2025SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmarkcited by 5
  14. 2025Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusioncited by 3
  15. 2025IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detectioncited by 2
  16. 2026VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detectioncited by 2
  17. 2025Learning Multi-view Multi-class Anomaly Detectioncited by 1
  18. 2025Unsupervised Multi-View Visual Anomaly Detection via Progressive Homography-Guided Alignmentcited by 1
  19. 2026URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detectioncited by 1

Built on by

Not cited yet.