ABSTRACT:This paper proposes a method for the reconstruction of city buildings with automatically derived textures that can be directly used for façade element classification. Oblique and nadir aerial imagery recorded by a multi-head camera system is transformed into dense 3D point clouds and evaluated statistically in order to extract the hull of the structures. For the resulting wall, roof and ground surfaces high-resolution polygonal texture patches are calculated and compactly arranged in a texture atlas without resampling. The façade textures subsequently get analyzed by a commercial software package to detect possible windows whose contours are projected into the original oriented source images and sparsely ray-casted to obtain their 3D world coordinates. With the windows being reintegrated into the previously extracted hull the final building models are stored as semantically annotated CityGML "LOD-2.5" objects.

Aerial imaging systems increasingly gain oblique viewing capabilities. Through these passive systems, photogrammetric 3D point clouds of a scene become available in addition to traditional vertical 2.5D information. In the field of urban reconstruction, this complementary information seeks for robust and automated fusion methods in order to derive 3D building geometry as well as topology in larger scales. It is sequentially shown how to get from façade planes over building footprints to roof reconstruction including overhangs. Façade planes are extracted from a photogrammetric high-resolution 3D point cloud. Local regression methods in 2D space are used to determine the local direction and a criterion for the local linearity of the point cloud. Based on these two parameters, the 3D point cloud is segmented according to which façade it belongs to. From the segmented point cloud, building footprints are extracted as polygons. Similar to cadaster information, those polygons, along with a traditional digital surface model (DSM), serve for one thing as the basis for overhang determination which is performed by fitting polynoms on the outside of façades and using their inflection points as overhang boundary. For another thing, they serve as roof areas which are segmented, topologically described and geometrically modelled. Again local regression methods are used but this time in 3D space in order to segment roof parts. Subsequently, the roof topology is derived using region growing methods. The final building models hold both, geometrical and topological properties.

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We present a workflow for seamless real-time navigation and 3D thermal mapping in combined indoor and outdoor environments in a global reference frame. The automated workflow and partly real-time capabilities are of special interest for inspection tasks and also for other time-critical applications. We use a hand-held integrated positioning system (IPS), which is a real-time capable visual-aided inertial navigation technology, and augment it with an additional passive thermal infrared camera and global referencing capabilities. The global reference is realized through surveyed optical markers (AprilTags). Due to the sensor data’s fusion of the stereo camera and the thermal images, the resulting georeferenced 3D point cloud is enriched with thermal intensity values. A challenging calibration approach is used to geometrically calibrate and pixel-co-register the trifocal camera system. By fusing the terrestrial dataset with additional geographic information from an unmanned aerial vehicle, we gain a complete building hull point cloud and automatically reconstruct a semantic 3D model. A single-family house with surroundings in the village of Morschenich near the city of Jülich (German federal state North Rhine-Westphalia) was used as a test site to demonstrate our workflow. The presented work is a step towards automated building information modeling.

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