A Multiresolution Approach to Model-Based 3-D Surface Quality Inspection

We propose a novel model-based surface approximation method for three-dimensional (3-D) surface quality inspection that combines a machine learning approach with multiresolution paradigms. Acceptable surface deviations are modeled by learning a number of 3-D measurements of tolerance samples. At the same time, areas with high surface details are hierarchically refined, allowing an improved spatial localization of the surface model. The method is based on a dual eigenvalue decomposition, which leads to fast computation for large datasets of ordered 3-D point clouds. The proposed algorithm is easy to configure and requires few parameters by automatically determining the areas for local refinement. It yields a better defect detection on deformable parts and parts with high tolerance ranges, especially on critical areas with high surface curvature. Experimental results show the effectiveness compared to model-based approaches without multiresolution as well as nonmodel-based methods. Examples are given for successful defect detection where previous methods have failed.