3D Feature Detector-Descriptor Pair Evaluation on Point Clouds

In recent years, computer vision research has focused on extracting features from 3D data. In this work, we reviewed methods of extracting local features from objects represented in the form of point clouds. The goal of the work was to make theoretical overview and evaluation of selected point cloud detectors and descriptors. We performed an experimental assessment of the repeatability and computational efficiency of individual methods using the well known Stanford 3D Scanning Repository database with the aim of identifying a method which is computationally-efficient in finding good corresponding points between two point clouds. We also compared the efficiency of detector-descriptor pairing showing that the choice of a descriptor affects the performance of the object recognition based on the descriptor matching. We summarized the results into graphs and described them with respect to the individual tested properties of the methods.