Extraction of Multi-Scale Geometric Features for Point Cloud Classification

Light Detection and Ranging (LiDAR) techniques is an efficient way of obtaining 3D information of complex urban scenes. However, automatically and efficiently interpreting acquired 3D points is still a challenging task. For achieving an excellent semantic interpretation of point clouds, the extraction of distinctive and reliable geometric features often plays a vital role. In this paper, we propose a method generating features from the local vicinity of different sizes and combine them for a better feature representation. To evaluate the proposed method, experiments were conducted using Li-DAR point cloud dataset and compared with that using single scale feature extraction methods.