Pre-processing is essential for processing the row data point clouds which acquired using a 3D laser scanner as a modern technique to digitize and reconstruct the surface of the 3D objects in reverse engineering applications. Due to the accuracy limitation of some 3D scanners and the environmental noise factors such as illumination and reflection, there are some noised data points associated with the row point clouds, so, in the present paper, a preprocessing algorithm has been proposed to determine and delete the unnecessary data as noised points and save the remaining data points for the surface reconstruction of 3D objects from its point clouds which acquired using the 3D laser scanner (Matter and Form). The proposed algorithm based on the assessment of tangent continuity as a geometrical feature and criteria for the contiguous points. A MATLAB software has been used to construct a program for the proposed point clouds pre-processing algorithm, the validity of the constructed program has been proved using geometrical case studies with different shapes. The application results of the proposed tangent algorithm and surface fitting process for the suggested case studies were proved the validity of the proposed algorithm for simplification of the point clouds, where the percent of noised data which removed according to the proposed tangent continuity algorithm which achieved a reduction of the total points to a percentage of (43.63%), and (32.01%) for the studied case studies, from the total number of data points in point cloud for first and second case study respectively.
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).
Abstract Although the rapid development in reverse engineering techniques, 3D laser scanners can be considered the modern technology used to digitize the 3D objects, but some troubles may be associate this process due to the environmental noises and limitation of the used scanners. So, in the present paper a data pre-processing algorithm has been proposed to obtain the necessary geometric features and mathematical representation of scanned object from its point cloud which obtained using 3D laser scanner (Matter and Form) through isolating the noised points. The proposed algorithm based on continuous calculations of chord angle between each adjacent pair of points in point cloud. A MATLAB program has been built to perform the proposed algorithm which implemented using a suggested case studies with cylinder and dome shape. The resulted point cloud from application the proposed algorithm and result of surface fitting for the case studies has been proved the proficiency of the proposed chord angle algorithm in pre-processing of data points and clean the point cloud, where the percent of data which was ignored as noisy data points according to proposed chord angle algorithm was arrived to (81.52%) and (75.01%)of total number of data points in point cloud for first and second case study respectively.
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