3D vision-based anomaly detection in manufacturing: A survey

Abstract Surface quality monitoring of manufacturing products is critical for manufacturing industries to ensure product quality and production efficiency. With the rapid development of 3D scanning technology, high-density 3D point cloud data can be generated by 3D scanners in complex manufacturing systems. However, due to the challenges of complex surface modeling and various types, it lacks effective surface anomaly detection methods that can meet the practical requirements regarding detection accuracy and speed. This survey aims to review the surface anomaly detection methodology of manufacturing products based on 3D machine vision. Specifically, the machine learning methodologies will be systematically reviewed for 3D point cloud data modeling and anomaly detection. Related public data sets for this research are also summarized. Finally, the future research directions are pointed out.