Model-based analysis and evaluation of point sets from optical 3D laser scanners

The digitalization of real-world objects is of vital importance in various application domains. This method is especially applied in industrial quality assurance to measure the geometric dimension accuracy. Furthermore, geometric models are the very foundation of contemporary three-dimensional computer graphics. In addition to create new models by using a modeling suite, the use of 3D laser scanners has recently become more and more common. To reconstruct objects from laser scan data, usually very large data sets have to be processed. In addition, the generated point clouds usually contain a considerable amount of errors. Therefore, it is necessary to optimize the data for further processing. Compared to algorithms that interactively manipulate point clouds through an approximation with polygonal meshes, we aim to automatically correct each measurement individually and directly integrate the methods into the measurement process. In addition to traditional methods which usually assume point clouds as unstructured, this work introduces techniques for the extraction of common data structures from optical 3D scanners. Based on this information, procedures are developed to enable automatable procedures of scan data optimization and evaluation. The feasibility of the proposed methods is shown at the example of different real-world objects and industrial applications.