Object Extraction from Architecture Scenes through 3D Local Scanned Data Analysis

Terrestrial laser scanning becomes a standard way for acquiring 3D data of complex outdoor objects. The processing of huge number of points and recognition of different objects inside become a new challenge, especially in the case where objects are included. In this paper, a new approach is proposed to classify objects through an analysis on shape information of the point cloud data. The scanned scene is constructed using k Nearest Neighboring (k-NN), and then similarity measurement between points is defined to cluster points with similar primitive shapes. Moreover, we introduce a combined geometrical criterion to refine the over-segmented results. To achieve more detail information, a residual based segmentation is adopted to refine the segmentation of architectural objects into more parts with different shape properties. Experimental results demonstrate that this approach can be used as a robust way to extract different objects in the scenes.