Scattered Point Cloud Simplification Algorithm Integrating k-means Clustering and Hausdorff Distance

Aiming at the incomplete retention of features during the point cloud data procession by point cloud simplification algorithm, and data holes caused by small-curvature point cloud simplification algorithm, this paper proposes a new point cloud simplification algorithm integrated k-means clustering and Hausdorff distance. The topological adjacency is established in the new simplification algorithm based on the OcTree algorithm.Then the principal curvatures of all point cloud is calculated and the Hausdorff distance of the principal curvatures is calculated, and the Hausdorff distance threshold set by the requirements of the reduced target is used to extracted the point cloud feature. Finally, k-means clustering is performed on non-feature regions to extract feature points, and the extracted feature points are merged to obtain reduced results. Results show that the proposed algorithm can retain the feature information of the model more completely and avoid the void phenomena.