Knowledge based building facade reconstruction from laser point clouds and images

Abstract

Various applications demand realistic 3D city models. For urban planning, analyzing in a 3D virtual reality world is much more efficient than imaging the 2D information on maps. For public security, accurate 3D building models are indispensable to make strategies during emergency situations. Navigation systems and virtual tourism also benefit from realistic city models. Manual creation of city models is undoubtedly a rather time consuming and expensive procedure. On one hand, images are for long the only data source for geometric modelling, while recovering of 3D geometries is not straightforward from 2D images. On the other hand, there are enormous amounts of objects (for example buildings) to be reconstructed, and their structures and shapes show a great variety. There is a lack of automated approaches to understand the building structures captured by data. The rapid development of cities even adds to the cost of manual city model updating. In recent years, laser scanning has been proven a successful technology for reverse engineering. The terrestrial laser point clouds are especially useful for documenting building facades. With the considerable high point density and the explicit 3D coordinates of terrestrial laser point clouds, it is possible to recover both large structures and fine details on building facades. The latest developments of mobile laser scanning technology also make it more cost-effective to take large-scale laser scanning over urban areas. This PhD research aims at reconstructing photorealistic building facade models from terrestrial laser point clouds and close range images, with a largely automatic process. A knowledge base about building facade structures is established first, where several important building features (wall, door, protrusion, etc.) are defined and described with their geometric properties and spatial relationships. Then constraints for feature extraction are derived from the knowledge base. After a laser point cloud is segmented into planar segments by surface a growing segmentation algorithm, each segment is compared with the feature constraints to determine the most likely feature type for each segment. The feature extraction method works fine for all facade features except for windows, because there are usually insufficient laser points reflected from window glass. Instead, windows are reconstructed from the holes on the wall features. Then outline polygons or B-spline surfaces are fit to all feature segments, and the parts without laser points are hypothesized