Point cloud simplification for the boundary preservation based on extracted four features

This paper proposes a simplification algorithm based on four feature parameters, aiming at solving the problem that the edge features cannot be retained due to the incompletely extracted sharp features during point cloud simplification. Firstly, K neighborhood searching is carried out for point cloud, and K neighborhood points are quickly found by a dynamic grid method. Then, four features including: the curvature of the point, the average of the normal angle of a point from a neighborhood point, the average distance between the point and the neighborhood point and the distance between the point and the center of gravity of the neighborhood point, are calculated according to the K neighborhood of the data point. The four parameters are used to define the feature discrimination parameters and feature thresholds, to compare the size and extract the feature points; finally, the non-feature points are reduced twice by the method of the bounding box, and the reduced point cloud and feature points are spliced to achieve the purpose of simplification. The experimental results show that the distance between the point and the center of gravity of the neighborhood has a great influence on the simplified model boundary, which effectively guarantees the accuracy of the simplified model.