Abstract
There are many applications for 3D city models, e.g., in visualizations, analysis, and simulations; each one requiring a certain level of detail to be effective. The overall trend goes towards including various kinds of anthropogenic and natural objects therein with ever increasing geometric and semantic details. A few years back, the featured 3D building models had only coarse roof geometry. But nowadays, they are expected to include detailed roof superstructures like dormers and chimneys. Several methods have been proposed for the automatic reconstruction of 3D building models from airborne based point clouds. However, they are usually unable to reliably recognize and reconstruct small roof superstructures as these objects are often represented by only few point measurements, especially in low-density point clouds. In this paper, we propose a recognition and reconstruction approach that overcomes this problem by identifying and simultaneously reconstructing regularized superstructures of similar shape. For this purpose, candidate areas for superstructures are detected by taking into account virtual sub-surface points that are assumed to lie on the main roof faces below the measured points. The areas with similar superstructures are detected, extracted, grouped together, and registered to one another with the Iterative Closest Point (ICP) algorithm. As an outcome, the joint point density of each detected group is increased, which helps to recognize the shape of the superstructure more reliably and in more detail. Finally, all instances of each group of superstructures are modeled at once and transformed back to their original position. Because superstructures are reconstructed in groups, symmetries, alignments, and regularities can be enforced in a straight-forward way. The validity of the approach is presented on a number of example buildings from the Vaihingen test data set.
Highlights
For several years, 3D city models assume a central role in urban and regional planning, surveying, navigation, and telecommunications and allow in the environmental field precise analyses and simulations of pollutant, flood and noise propagation, and solar potential
We present in this paper a new approach for the fully-automatic reconstruction of regularized roof superstructures from low-density LIDAR data, which might feature partially occluded areas
The proposed approach is tested both on generated data (1.5-4 points/m2) and on several buildings located in residential parts of the Vaihingen test data set (4-6 points/m2), which is provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) (Cramer, 2010)
Summary
3D city models assume a central role in urban and regional planning, surveying, navigation, and telecommunications and allow in the environmental field precise analyses and simulations of pollutant, flood and noise propagation, and solar potential. Most of them are not applicable in practice for a largescale reconstruction process due, for example, to missing additional data sources To solve this issue, we present in this paper a new approach for the fully-automatic reconstruction of regularized roof superstructures from low-density LIDAR data, which might feature partially occluded areas. Instead of reconstructing each superstructure independently from one another, our proposed approach detects first all instances of a roof superstructure and reconstructs them afterwards at once For this purpose, a point cloud registration technique based on the Iterative Closest Point (ICP) algorithm (see section 3) is presented. A point cloud registration technique based on the Iterative Closest Point (ICP) algorithm (see section 3) is presented It considers basic building knowledge (e.g. local directions, symmetries, repetitive structures, etc.) and increases locally the point density so that a more accurate model can be generated. The double-blind peer-review was conducted on the basis of the full paper
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