Texturizing and refinement of 3D city models with mobile devices

In this thesis, I investigate the problem of building recognition from images and video streams of mobile devices to texturize and refine an existing 3D city model. These city models have different origins and so different levels of detail. Existing approaches for this problem are analyzed and compared. Since no suitable approach was found, a new one has to be created. Supported by GPS and Gyroscope sensor data there are multiple possibilities to analyze images of the video stream on a 2D and 3D basis. Different ways to extract buildings from images are presented including model refinement. These are based on computer vision technologies such as edge detection on images to detect walls, pure use of sensor data by creating an overlay to the video stream with the 3D model renderer from current position by a server and use of structure from motion algorithms to create point clouds and extract a building from them. Each of the different detectors yields in successful results depending on different properties of a building such as e.g. the presence of adjoining buildings or accuracy of the 3D model. The edge detection modes yield to the highest accuracy, followed by the extraction from point clouds and pure sensor data. Also future work is presented to expand the shown approaches.