POINTS CLOUD PRE-PROCESSING AND SAMPLING BASED ON DISTANCE ALGORITHM TECHNIQUE

Although the rapid development of reverse engineering techniques such as a modern 3D laser scanners, but can’t use this techniques immediately to generate a perfect surface model for the scanned parts, due to the huge data, the noisy data which associated to the scanning process, and the accuracy limitation of some scanning devices, so, the present paper present a points cloud pre-processing and sampling algorithms have been proposed based on distance calculations and statistical considerations to simplify the row points cloud which obtained using MATTER and FORM 3D laser scanner as a manner to obtain the required geometrical features and mathematical representation from the row points cloud of the scanned object through detection, isolating, and deleting the noised points. A MATLAB program has been constructed for executing the proposed algorithms implemented using a suggested case study with non-uniform shape. The results were proved the validity of the introduced distance algorithms for pre-processing and sampling process where the proficiency percent for pre-processing was (18.65%) with a single attempt, and the counted deviation value rang with the sampling process was (0.0002-0.3497mm).