3D Point Cloud Data

This paper presents PointSGRADE, a novel sparse learning framework with graph representation for anomaly detection on smooth free-form surfaces using unstructured 3D point cloud data. The methodology addresses challenges such as irregular data structures and variant anomaly patterns, offering a computationally efficient solution.

This paper proposes a novel Bayesian network-based methodology for anomaly detection in fabricated artifacts using unstructured 3D point cloud data. The approach addresses challenges such as nonexistence of global coordinate ordering, variant anomaly patterns, and the presence of outliers.

This paper proposes a tensor voting-based approach for classifying surface anomalies on artifacts using 3D point cloud data, addressing challenges such as complex data representation, high dimensionality, and inconsistent point sizes.