Size Measurement of Thin-walled Parts Based on Point Cloud Classification Simplification Strategy

Point clouds can lead to the problem of losing key geometric features during the simplification process. A point cloud simplification algorithm based on classification simplification strategy is proposed for this purpose. Firstly, the point cloud is simplified to achieve a balance between simplification and geometric features. Then, the improved region growing algorithm is used to accurately segment the point cloud and simplify the sampling of different degrees of voxels. The integrity of the key geometric features is ensured, and the simplified accuracy is evaluated by the RMSE. Finally, the measurement verification is carried out on the circular hole of the thin-walled part. The results show that the proposed method is superior to the traditional voxel down sampling, and can deal with complex multi-feature point cloud models. It can effectively solve the problem of missing feature points in the traditional simplification process and meet the measurement dimensional accuracy requirements of industrial digital manufacturing.