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Automatic Segmentation of Urban Point Clouds Based on the Gaussian Map

A comprehensive method to segment urban point clouds based on the Gaussian map is presented. The normals of point clouds are firstly mapped to the Gaussian sphere and then partitioned into groups using a mean shift clustering algorithm. Next, a distance‐based clustering method is presented to tackle overlapping surfaces which avoids under‐segmentation. Based on the properties of the Gaussian map and the geometric information of the points, primitive shapes such as planes, cylinders, cones and spheres are recognised. Trees, cars, street lights and other objects are then segmented by using the distance‐based clustering method after removing the planes, cylinders, cones and spheres from the large urban scenes. Finally, a refinement process based on primitive shapes is implemented to improve the segmentation results which effectively avoids over‐segmentation. Experimental results demonstrate that the proposed method can be used as a robust way to segment urban point clouds based on the Gaussian map.