New Segmentation Method Combining Three-Dimensional Point Cloud Skeleton Points and Feature Points

Aiming at the problem of low accuracy of the segmentation algorithm for three-dimensional (3D) point cloud data, a new segmentation algorithm combining point cloud skeleton points and external feature points is proposed. This method can effectively segment local small-scale convex objects, which cannot be segmented by traditional methods. This would make the segmentation of (3D) point cloud data more perfect and provide a new idea for the segmentation of (3D) point clouds. In this C++ paper, and its open source point cloud library arc used to program. First, L-1 median algorithm is used to extract skeleton points from (3D) point clouds. At the same time, feature points arc extracted by scale-invariant feature transform algorithm. Then, a segmentation plane is constructed based on skeleton points and feature points, segmentation is conducted, and the remaining feature points arc detected. At last, a segmentation plane is constructed again for segmentation, therefore getting the final result. Experimental results show that the algorithm can efficiently segment small-scale convex surface of (3D) point clouds and improve the accuracy of segmentation.