ENHANCEMENT OF GENERIC BUILDING MODELS BY RECOGNITION AND ENFORCEMENT OF GEOMETRIC CONSTRAINTS
Many buildings in 3D city models can be represented by generic models, e.g. boundary representations or polyhedrons, without expressing building-specific knowledge explicitly. Without additional constraints, the bounding faces of these building reconstructions do not feature expected structures such...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-3/333/2016/isprs-annals-III-3-333-2016.pdf |
| Summary: | Many buildings in 3D city models can be represented by generic models, e.g. boundary representations or polyhedrons, without expressing
building-specific knowledge explicitly. Without additional constraints, the bounding faces of these building reconstructions do
not feature expected structures such as orthogonality or parallelism. The recognition and enforcement of man-made structures within
model instances is one way to enhance 3D city models. Since the reconstructions are derived from uncertain and imprecise data, crisp
relations such as orthogonality or parallelism are rarely satisfied exactly. Furthermore, the uncertainty of geometric entities is usually
not specified in 3D city models. Therefore, we propose a point sampling which simulates the initial point cloud acquisition by airborne
laser scanning and provides estimates for the uncertainties. We present a complete workflow for recognition and enforcement
of man-made structures in a given boundary representation. The recognition is performed by hypothesis testing and the enforcement
of the detected constraints by a global adjustment of all bounding faces. Since the adjustment changes not only the geometry but also
the topology of faces, we obtain improved building models which feature regular structures and a potentially reduced complexity. The
feasibility and the usability of the approach are demonstrated with a real data set. |
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| ISSN: | 2194-9042 2194-9050 |