Automatic Generation of Digital Building Models for Complex Structures from Lidar Data

Automated and reliable 3D reconstruction of man-made structures is important for various applications in virtual reality, city modeling, military training, etc. This paper is concerned with the automated generation of Digital Building Models (DBM) associated with complex structures comprised of small parts with different slopes, sizes, and shapes, from a LiDAR point cloud. The proposed methodology consists of a sequence of four steps: ground/non-ground point separation; building hypothesis generation; segmentation of planar patches and intermediate boundary generation; and boundary refinement and 3D wire frame generation. First, a novel ground/non-ground point classification technique is proposed based on the visibility analysis among ground and non-ground points in a synthesized perspective view. Once the LiDAR point cloud has been classified into ground and non-ground points, the non-ground points are analyzed and used to generate hypotheses of building instances based on the point attributes and the spatial relationships among the points. The third step of the proposed methodology segments each building hypothesis into a group of planar patches while simultaneously considering the attribute similarity and the spatial proximity among the points. The intermediate boundaries for segmented clusters are produced by using a modified convex hull algorithm. These boundaries are used as initial approximations of the planar surfaces comprising the building model of a given hypothesis. The last step of the proposed methodology utilizes these initial boundaries to come up with a refined set of boundaries, which are connected to produce a wire frame representing the DBM. The performance of the proposed methodology has been evaluated using experimental results from real data. * Corresponding author.