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.