Analysis of Data Point Cloud Preprocessing and Feature Angle Detection Algorithm

Abstract

Background: The two main stages are utilized for feature extraction, from which the first stage consists of a penalty weight to the neighbor graph’s edges. The edge penalty weights are minimized by the neighbor sub-graph extraction to produce the set of feature patterns. For noisy data, the second stage is helpful. Methodology: In order to realize the measurement of the geometric dimensions of the ship block, this paper uses the theory of computer vision and reverse engineering to obtain the data of the segmented- hull with the method of digitizing the physical parts based on the vision, and processes the data by using the relevant knowledge of reverse engineering. Result: The results show that the efficiency of the edge extraction algorithm based on mathematical morphology is 30% higher than that of the mesh generation method. An adaptive corner detection algorithm based on the edge can adaptively determine the size of the support area and accurately detect the corner position. Conclusion: According to the characteristics of the point cloud of ship hull segment data, an adaptive corner detection algorithm based on the edge is adopted to verify its feasibility.