Anomaly detection for fabricated artifact by using unstructured 3D point cloud data

3D point cloud data has been widely used in surface quality inspection to measure fabricated artifacts, allowing the high density and precision of measurements and providing quantitative 3D geometric characteristics for anomalies. Unlike structured 3D point cloud data, unstructured 3D point cloud data can capture the surface geometry completely. However, anomaly detection by using unstructured 3D point cloud data is more challenging, due to the nonexistence of global coordinate ordering and the difficulty of mathematically modeling anomalies and discriminating outliers. To deal with these challenges, this article formulates the anomaly detection problem into a probabilistic framework. By categorizing points into three types, i.e., reference surface point, anomaly point, and outlier point, a novel Bayesian network is proposed to model the unstructured 3D point cloud data. The variational expectation-maximization algorithm is used to estimate parameters and make inference on the unknown types of points. Both simulation and real case studies demonstrate the accuracy and robustness of the proposed method.