Surface Defect Identification using Bayesian Filtering on a 3D Mesh
This addresses quality control in manufacturing by enabling high-precision defect identification with affordable sensors, though it appears incremental.
The paper tackles automated surface defect detection by integrating CAD model knowledge with point cloud data from stereo/depth cameras, achieving sub-millimeter standard deviation convergence with about 50 samples.
This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications.