Abstract:
Geometrically and topologically correct 3D building models are required to satisfy the
increasing demand in, for instance 3D cadaster, virtual reality, emergency response,
robot navigation, and urban planning. Airborne Laser Scanning (ALS) is still the
preferred data acquisition system for automated building modeling. Although ALS point
clouds are useful for a highly automated processing workflow with high vertical
accuracy, their sparse point distribution reduces the planimetric accuracy of model
boundaries significantly. In comparison to the ground sampling of digital aerial images
to the centimeter level, the planimetric accuracy of building models derived from point
clouds is severely limited. Since point clouds and images have rather complementary
properties, the integration of these two data sources leads to building models of high
vertical accuracy, as well as planimetrical accuracy. In this study, a new framework for
the automatic reconstruction of building models by integrating ALS point clouds and
digital aerial image data is proposed. Topology preserving 3D roof models is first
derived from point clouds. These models are subsequently refined to increase the
planimentric accuracy with image data. In addition, some of the topological
inaccuracies of the initial roof models are rectified. A novel approach employing a cycle
graph analysis is introduced to generate the topology preserving roof models from
point clouds. Initial and refined roof models derived from the developed schemes are
analyzed with the ISPRS benchmark test data. The results of the three test scenes
showed that both methods are acceptable, and can be used with more complex urban
scenes. While proving the robustness of the cycle graph approach by the initial results,
the refined models demonstrate that image integration improves the planimentric
accuracy significantly, with almost 100% topological and geometrical correctness.
