In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (<i>linear structures, planar structures</i> and <i>volumetric structures</i>) as well as a reference labeling with respect to five semantic classes (<i>Wire, Pole/Trunk, Façade, Ground</i> and <i>Vegetation</i>) is available.
3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering
