A new point cloud simplification method with feature and integrity preservation by partition strategy

•Simplify the point cloud by using partitioning strategy.•Introduce a region growing segmentation method to divide the point cloud into feature regions and non-feature regions.•Use curvature and point spacing to measure the features of points.•Use normal difference, information entropy, and voxel down-sampling to get edge points, feature points, and non-feature points of the point cloud, respectively. The advancement of 3D scanning technology has gradually brought to light the issue of complex and time-consuming processing of high-density point cloud data. To address this need, this article proposes a method for point cloud simplification based on the concept of partitioning, which divides the point cloud's points into edge points, feature points, and non-feature points. On the basis of the normal angular difference, the edge points are extracted from the entire point cloud. The point cloud is then segmented by curvature into feature and non-feature regions using a region growing segmentation method. The feature points are determined by calculating the information entropy of the points in the feature region, whereas the non-feature points are extracted by voxel down-sampling the non-feature points. The experiment demonstrates that the proposed method effectively preserves the features and integrity of the point cloud while requiring less computational effort.