Abstract: A fairing skin repair method based on measured wing data includes fairing skin registration. Data set P1 through denoising and filtering wing point cloud data is reorganized to obtain a key point set P. A histogram feature descriptor in a normal direction of any key point in set P and a skin point cloud data Q is calculated. Euclidean distance between feature descriptors of two points is calculated through K-nearest neighbor algorithm, and points with high similarity are added into a set M. A clustering is performed on set M using a Hough voting algorithm to obtain a local point cloud set P? in set P. The method includes fairing skin repair. The boundary line of the point frame is projected onto Q, and a distance between a projection line on the point cloud and the boundary line is calculated to obtain an amount of skin to be repaired.

Abstract: A fairing skin repair method based on measured wing data includes fairing skin registration. Data set P1 through denoising and filtering wing point cloud data is reorganized to obtain a key point set P. A histogram feature descriptor in a normal direction of any key point in set P and a skin point cloud data Q is calculated. Euclidean distance between feature descriptors of two points is calculated through K-nearest neighbor algorithm, and points with high similarity are added into a set M. A clustering is performed on set M using a Hough voting algorithm to obtain a local point cloud set P? in set P. The method includes fairing skin repair. The boundary line of the point frame is projected onto Q, and a distance between a projection line on the point cloud and the boundary line is calculated to obtain an amount of skin to be repaired.