Unsupervised Visual-to-Geometric Feature Reconstruction for Vision-Based Industrial Anomaly Detection

Unsupervised Visual-to-Geometric Feature Reconstruction for Vision-Based Industrial Anomaly Detection

Industrial anomaly detection involves identifying abnormal regions in products and plays a crucial role in quality inspection. While 2D image-based anomaly detection has been extensively explored, combining two-dimensional (2D) images with three-dimensional (3D) point clouds remains less studied. Ex...

Full description

Saved in:
Bibliographic Details
Main Authors: Dinh-Cuong Hoang, Phan Xuan Tan, Anh-Nhat Nguyen, Duc-Thanh Tran, Van-Hiep Duong, Anh-Truong Mai, Duc-Long Pham, Khanh-Toan Phan, Minh-Quang Do, Ta Huu Anh Duong, Tuan-Minh Huynh, Son-Anh Bui, Duc-Manh Nguyen, Viet-Anh Trinh, Khanh-Duong Tran, Thu-Uyen Nguyen
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Industrial anomaly detection
image-based anomaly detection
3D point clouds
Online Access:https://ieeexplore.ieee.org/document/10820339/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10820339/

Similar Items