Research on point cloud simplification method for retaining sharp features of turbine blades

To address the issue of large data volume and information redundancy in turbine blade point cloud data acquired through industrial computerized tomography scanning, which leads to difficulties in subsequent data processing, this paper proposes a point cloud simplification method that preserves sharp features. This method segments feature regions based on curvature meshes, calculates curvature feature values, identifies and retains feature points, and conducts a secondary reduction on non-feature points. By merging the feature points with the simplified non-feature points, the method achieves simplification of high-density point cloud data while preserving sharp features. Experiments indicate that this approach can effectively remove redundant points from turbine blades, retain sharp features of the point cloud, and ensure the precision requirements of the point cloud are met.