A Fast Flatness Deviation Evaluation Algorithm for Point Cloud Data

This paper proposes and develops a novel method, namely the Partially Iterative Algorithm (PIA), for highspeed assessment of flatness deviation for point cloud data, which is typically measured data obtained by advanced instruments for precision manufacturing, such as optical scanners and industrial computed tomography. Firstly, an enhanced flatness deviation model is established based on the minimum zone principle, which is strictly adhered to the latest ISO definition. Secondly, the proposed method is detailed, including the Dynamic Point Set (DPS), the update scheme, and the terminal condition. Thirdly, comparisons are conducted with typical methods for flatness deviation assessment, along with a practicability test via the simulated dataset and measuring dataset. The results show that the proposed method can accurately and rapidly assess flatness deviation on point cloud data with massive measuring points.