Feature detection in unorganized pointclouds

Feature detection in pointclouds is becoming increasingly important for a wide range of applications. For example fields of robotics, autonomous driving and medical image processing are using acquisition sensors with 3 dimensional output. These points are usually defined by x, y, and z coordinates and represents the outer surface or shell of an object. Challenges are to develop methods for effective ways to get just the relevant information in pointclouds and accelerate postprocessing steps. The approaches presented in this paper are based on innervolumetric 3D data, which are described by chaotically organized points. The data used are generated by the layer-by-layer acquisition and composition of individual point clouds in additive manufacturing. The data is merged, preprocessed and subsequently the characteristics are extracted.