An improved feature point selection algorithm for point cloud data

Abstract

At present, curve and surface fitting is widely used in three-dimensional measurement, industrial design, archaeology, medicine and other fields, and curve and surface fitting has also become a hot spot and a difficulty at present. The surface point cloud data scanned by high-precision 3D laser scanning instruments on site are often complex, and the data are relatively dense for curves. If the approximation fitting is used, complex information may not be reflected enough, and the interpolation fitting may produce over-fitting phenomenon. This paper proposes a feature point selection algorithm, which is more targeted for dense point cloud data than the general cubic B-spline interpolation algorithm. The feature point selection algorithm can retain feature points and remove non-feature points and minimize the number of fitting segments on the premise of meeting the accuracy requirements of the final fitting curve.