3 DIMENSIONAL POINT CLOUD FILTERING USING DIFFERENTIAL EVOLUTION ALGORITHM

The ability to model an object or an environment using 3-dimensional point cloud data is very important for some areas such as photogrammetry, remote sensing, material processing and production, reverse engineering, construction industry, virtual reality and medicine. However, 3-dimensional point cloud data obtained with existing technologies contain some noise due to the nature of the measurement device and man-made errors. Therefore, it has great importance to filter raw point cloud data or surface elements derived from point cloud data to increase the quality of the 3D model. In this study, a filtering method was developed based on plane fitting by differential evolution algorithm to filter noisy point cloud data. The proposed heuristic algorithm-based filtering method is compared with the singular value decomposition method, which is frequently used in literature to obtain the plane parameters. Both visual and numerical results show that the plane fitting method based on the differential evolution algorithm is more successful to remove noise than the classical filtering method based on singular value decomposition.