Feature Line Generation and Regularization From Point Clouds

The shape of the object is mainly described by feature points and lines. Since a feature point can be described by the intersection of two feature lines, feature lines are the key to determine the contour of the object. In this article, a novel method for the generation and regularization of point cloud feature line is presented, which consists of two main steps: extraction of the outline points according to the property of vectors distribution and cluster, feature points are sorted according to the vector deflection angle and distance and they are fitted using the improved cubic b-spline curve fitting algorithm. The performance of the proposed method is evaluated with both large and small point clouds acquired by terrestrial laser scanning devices in real-world scenes. The results show that the proposed method and the analysis of geometrical properties of neighborhoods (AGPN) method achieve very similar performance in the case of planar objects, accurately extracting the outline points of objects. However, in the presence of a curved surface, the proposed method significantly outperforms the existing methods in detecting outline points. The outlines are regularized by the improved cubic b-spline and it is superior to the traditional cubic b-spline curve fitting algorithm.