Point cloud segmentation for urban scene classification
High density point clouds of urban scenes are used to identify object classes like buildings, vegetation, vehicles, ground, and water. Point cloud segmentation can support classification and further feature extraction provided that the segments are logical groups of points belonging to the same object class. A single segmentation method will typically not provide a satisfactory segmentation for a variety of classes. This paper explores the combination of various segmentation and post-processing methods to arrive at useful point cloud segmentations. A feature based on the normal vector and flatness of a point neighbourhood is used to group cluttered points in trees as well as points on surfaces in areas where the extraction of planes was not successful. Combined with segment merging and majority filtering large segments can be obtained allowing the derivation of accurate segment feature values. Results are presented and discussed for a 70 million point dataset over a part of Rotterdam.