Ongoing innovations in dense multi-view stereo image matching meanwhile allow for 3D data collection using image sequences captured from mobile mapping platforms even in complex and densely built-up areas. However, the extraction of dense and precise 3D point clouds from such street-level imagery presumes high quality georeferencing as a first processing step. While standard direct georeferencing solves this task in open areas, poor GNSS coverage in densely built-up areas and urban canyons frequently prevents sufficient accuracy and reliability. Thus, we use bundle block adjustment, which additionally integrates tie and control point information for precise georeferencing of our multi-camera mobile mapping system. Subsequently, this allows the adaption of a state-of-the-art dense image matching pipeline to provide a suitable 3D representation of the captured urban structures. In addition to the presentation of different processing steps, this paper also provides an evaluation of the achieved image-based 3D capture in a dense urban environment.
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