Curve Skeleton Extraction From 3D Point Clouds Through Hybrid Feature Point Shifting and Clustering

Curve skeleton is an important shape descriptor with many potential applications in computer graphics, visualization and machine intelligence. We present a curve skeleton expression based on the set of the cross-section centroids from a point cloud model and propose a corresponding extraction approach. We first provide the substitution of a distance field for a 3D point cloud model, and then combine it with curvatures to capture hybrid feature points. By introducing relevant facets and points, we shift these hybrid feature points along the skeleton-guided normal directions to approach local centroids, simplify them through a tensor-based spectral clustering and finally connect them to form a primary connected curve skeleton. Furthermore, we refine the primary skeleton through pruning, trimming and smoothing. We compared our results with several state-of-the-art algorithms including the rotational symmetry axis (ROSA) and L₁-medial methods for incomplete point cloud data to evaluate the effectiveness and accuracy of our method.