In the recent years, mobile LiDAR data has become an important data source for building mapping. However, it is challenging to extract building instances in residential areas where buildings of different structures are closely distributed and surrounded by cluttered objects such as vegetations. In this paper, we present a new "localization then segmentation" framework to tackle these problems. First, a hypothesis and selection method is proposed to localize buildings. Rectangle proposals which indicate building locations are generated using projections of vertical walls obtained by region growing. The selection of rectangles is formulated as a constrained maximization problem, which is solved by linear programming. Then, point clouds are divided into groups, each of which contains one building instance. A foreground-background segmentation method is then proposed to extract buildings from complex surroundings in each group. Based on the graph of points, an objective function which integrates local geometric features and shape priors is minimized by the graph cuts. The experiments are conducted in two large and complex scenes, Calgary and Kentucky residential areas. The completeness and correctness of building localization in the former dataset are 87.2% and 91.34%, respectively. In the latter dataset, the completeness and correctness of building localization are 100% and 96.3%, respectively. Based on the tests, our binary segmentation method outperforms existing methods regarding the Fl measure. These results demonstrate the feasibility and effectiveness of our framework in extracting instance-level residential buildings from mobile LiDAR point clouds in suburban areas.
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