Classification of mobile laser scanning point clouds of urban scenes exploiting cylindrical neighbourhoods

This paper presents our work on automated classification of Mobile Laser Scanning (MLS) point clouds of urban scenes with features derived from cylinders around points of consideration. The core of our method consists of spanning up a cylinder around points and deriving features, such as reflectance, height difference, from the points present within the cylindrical neighbourhood. Crucial in the approach is the selection of features from the points within the cylinder. An overall accuracy could be achieved, exploiting two bench mark data sets (Paris-rue-Madame and IQmulus & TerraMobilita) of 83% and 87% respectively.