mlsanityData to 14 Oct 2026

Multi-View Reconstruction with Global Context for 3D Anomaly Detection

arXiv 2507.21555 ↗First posted 29 Jul 2025cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

3D anomaly detection is critical in industrial quality inspection. While existing methods achieve notable progress, their performance degrades in high-precision 3D anomaly detection due to insufficient global information. To address this, we propose Multi-View Reconstruction (MVR), a method that losslessly converts high-resolution point clouds into multi-view images and employs a reconstruction-based anomaly detection framework to enhance global information learning. Extensive experiments demonstrate the effectiveness of MVR, achieving 89.6% object-wise AU-ROC and 95.7% point-wise AU-ROC on the Real3D-AD benchmark.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 20 references that are in the map. First posted on arXiv 29 Jul 2025.

Builds on

17 of 20 references.

References · 17

  1. 2023Vision Transformers Need Registerscited by 871
  2. 2019On the Convergence of Adam and Beyondcited by 841
  3. 2018Open3D: A Modern Library for 3D Data Processingcited by 721
  4. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
  5. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 93
  6. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  7. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 75
  8. 2023Stable and low-precision training for large-scale vision-language modelscited by 75
  9. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  10. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 63
  11. 2025Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detectioncited by 48
  12. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
5 more
  1. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  2. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  3. 2025Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defectscited by 15
  4. 2024PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Featurescited by 5
  5. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2

Built on by

Not cited yet.