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.
%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 from:markusenglich
%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/ifp},
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 from:markusenglich},
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
}