Urban Modeling Based on Segmentation and Regularization of Airborne Lidar Point Clouds

This paper presents an approach to process raw lidar 3-D point clouds over urban area and extract terrain, buildings and other urban features. In the initial step, “non-ground points” are separated from ground points using a one dimensional filtering process based on the slope between two consecutive points in the point cloud and the terrain elevation in the vicinity of the points. In the next step, the non-ground point dataset is processed to segment individual buildings. This is accomplished by using a 3-D regional growing approach. At the end of this step, each lidar point is attributed to a building. The first step towards building reconstruction is to obtain an approximate footprint of the building, which is accomplished by extracting the points on the building boundary by a modified convex hull algorithm. Once the footprint boundary points are found, their edges are regularized by using a least squares model to form the final building shape. Mathematic formulation of 3D region growing and boundary regularization is presented. Tests results of reconstructed buildings over complex urban areas are reported.