Extracting of Cross Section Profiles from Complex Point Cloud Data Sets

摘要

Point cloud data sets are widely used in design and manufacturing. Extracting 2D and 3D features from a point cloud is a field that many researchers are working on. In the present work, a slicing algorithm is implemented for segmenting a point cloud by parallel planes in the X, Y and Z directions and storing the point coordinates within each sectioned plane into separate files. After slicing, a new method is developed for filtering and extracting the outer boundary for each cross section. In the next step, this algorithm is combined with the density-based spatial clustering of applications with noise (DBSCAN) method to achieve a better boundary extraction result for complex outlines. The codes are written in Python (v. 3.7) and executed in Spyder using the Anaconda software package (v. 5.3.1). Complex case studies (*.STL lung model and a femur model) are used to illustrate the merits of this approach.