Super-voxel based segmentation and classification of 3D urban landscapes with evaluation and comparison

Abstract

Classification of urban range data into different object classes offers several challenges due to certain properties of the data such as density variation, inconsistencies due to holes and the large data size which requires heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from Lidar sensors is presented. The 3D point cloud is first segmented into voxels which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a newmetrics is presented which combines both segmentation and classification results simultaneously. The proposed method is evaluated on standard datasets using three different evaluation metrics. © Springer-Verlag Berlin Heidelberg 2014.