%0 Journal Article %1 Cavegn:2016:0099-1112:925 %A Cavegn, Stefan %A Haala, Norbert %D 2016 %J Photogrammetric Engineering & Remote Sensing %K journal review %N 12 %P 925-933 %R doi:10.14358/PERS.82.12.925 %T Image-Based Mobile Mapping for 3D Urban Data Capture %U https://www.ingentaconnect.com/content/asprs/pers/2016/00000082/00000012/art00013 %V 82 %X 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.

@article{Cavegn:2016:0099-1112:925, abstract = {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.}, added-at = {2021-12-06T10:10:15.000+0100}, author = {Cavegn, Stefan and Haala, Norbert}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/26fd20c2b91a92f4e0d2872696671be2d/markusenglich}, doi = {doi:10.14358/PERS.82.12.925}, interhash = {ee50689192271a737d93bf1cbfb3cda1}, intrahash = {6fd20c2b91a92f4e0d2872696671be2d}, issn = {0099-1112}, itemtype = {ARTICLE}, journal = {Photogrammetric Engineering & Remote Sensing}, keywords = {journal review}, number = 12, pages = {925-933}, parent_itemid = {infobike://asprs/pers}, publishercode = {asprs}, timestamp = {2021-12-06T09:10:15.000+0100}, title = {Image-Based Mobile Mapping for 3D Urban Data Capture}, url = {https://www.ingentaconnect.com/content/asprs/pers/2016/00000082/00000012/art00013}, volume = 82, year = 2016 }