Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Semi-automatic Generation of Historical Urban 3D Models at a Larger Scale Using Structure-from-Motion, Neural Rendering and Historical Maps
https://orcid.org/http://orcid.org/0000-0002-2456-9731
https://orcid.org/http://orcid.org/0000-0002-5639-2547
https://orcid.org/http://orcid.org/0000-0002-8714-6269
https://orcid.org/http://orcid.org/0000-0001-5396-1612
https://orcid.org/http://orcid.org/0000-0001-9344-912X
This paper presents two different strategies for the exploitation of historical photographs and maps and their usage in a three-dimensional (3D) and four-dimensional (4D) space. In the first approach, over 4000 historical photographs of Jena are collected via a citizen competition. These images are filtered in a semi-automatic way and processed in an adapted Structure-from-Motion workflow leading to multiple historical 3D models. As an innovative approach, two of the resulting reconstructions are refined using two different Neural Rendering approaches. For the first time, this enables the detailed seamless 3D visualization of sparse historical datasets, including defunct buildings. In cases where no photographs of buildings are available, an alternative strategy is demonstrated using historical maps. With an increasing amount of digitized historical maps, it becomes possible to segment and vectorize the building footprints for different points in time. The approach uses a semi-automatic workflow where a part of a historical map of Jena in 1936 is labeled manually in order to derive the remaining footprints automatically. At the moment, this labor-intensive step still has to be transferred to other historical maps to generate varying simple building models for different points in time. A combination of both approaches will allow the generation of detailed urban 4D models at a larger scale.
Semi-automatic Generation of Historical Urban 3D Models at a Larger Scale Using Structure-from-Motion, Neural Rendering and Historical Maps
https://orcid.org/http://orcid.org/0000-0002-2456-9731
https://orcid.org/http://orcid.org/0000-0002-5639-2547
https://orcid.org/http://orcid.org/0000-0002-8714-6269
https://orcid.org/http://orcid.org/0000-0001-5396-1612
https://orcid.org/http://orcid.org/0000-0001-9344-912X
This paper presents two different strategies for the exploitation of historical photographs and maps and their usage in a three-dimensional (3D) and four-dimensional (4D) space. In the first approach, over 4000 historical photographs of Jena are collected via a citizen competition. These images are filtered in a semi-automatic way and processed in an adapted Structure-from-Motion workflow leading to multiple historical 3D models. As an innovative approach, two of the resulting reconstructions are refined using two different Neural Rendering approaches. For the first time, this enables the detailed seamless 3D visualization of sparse historical datasets, including defunct buildings. In cases where no photographs of buildings are available, an alternative strategy is demonstrated using historical maps. With an increasing amount of digitized historical maps, it becomes possible to segment and vectorize the building footprints for different points in time. The approach uses a semi-automatic workflow where a part of a historical map of Jena in 1936 is labeled manually in order to derive the remaining footprints automatically. At the moment, this labor-intensive step still has to be transferred to other historical maps to generate varying simple building models for different points in time. A combination of both approaches will allow the generation of detailed urban 4D models at a larger scale.
Semi-automatic Generation of Historical Urban 3D Models at a Larger Scale Using Structure-from-Motion, Neural Rendering and Historical Maps
https://orcid.org/http://orcid.org/0000-0002-2456-9731
https://orcid.org/http://orcid.org/0000-0002-5639-2547
https://orcid.org/http://orcid.org/0000-0002-8714-6269
https://orcid.org/http://orcid.org/0000-0001-5396-1612
https://orcid.org/http://orcid.org/0000-0001-9344-912X
This paper presents two different strategies for the exploitation of historical photographs and maps and their usage in a three-dimensional (3D) and four-dimensional (4D) space. In the first approach, over 4000 historical photographs of Jena are collected via a citizen competition. These images are filtered in a semi-automatic way and processed in an adapted Structure-from-Motion workflow leading to multiple historical 3D models. As an innovative approach, two of the resulting reconstructions are refined using two different Neural Rendering approaches. For the first time, this enables the detailed seamless 3D visualization of sparse historical datasets, including defunct buildings. In cases where no photographs of buildings are available, an alternative strategy is demonstrated using historical maps. With an increasing amount of digitized historical maps, it becomes possible to segment and vectorize the building footprints for different points in time. The approach uses a semi-automatic workflow where a part of a historical map of Jena in 1936 is labeled manually in order to derive the remaining footprints automatically. At the moment, this labor-intensive step still has to be transferred to other historical maps to generate varying simple building models for different points in time. A combination of both approaches will allow the generation of detailed urban 4D models at a larger scale.
Semi-automatic Generation of Historical Urban 3D Models at a Larger Scale Using Structure-from-Motion, Neural Rendering and Historical Maps
Communic.Comp.Inf.Science
Münster, Sander (Herausgeber:in) / Pattee, Aaron (Herausgeber:in) / Kröber, Cindy (Herausgeber:in) / Niebling, Florian (Herausgeber:in) / Maiwald, Ferdinand (Autor:in) / Komorowicz, Dávid (Autor:in) / Munir, Iqra (Autor:in) / Beck, Clemens (Autor:in) / Münster, Sander (Autor:in)
Workshop on Research and Education in Urban History in the Age of Digital Libraries ; 2023 ; Munich, Germany
Research and Education in Urban History in the Age of Digital Libraries ; Kapitel: 7 ; 107-127
29.07.2023
21 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
4D , building models , historical photographs , Structure-from-Motion , Neural Rendering , historical maps , segmentation Computer Science , Computer Systems Organization and Communication Networks , Artificial Intelligence , Computer Imaging, Vision, Pattern Recognition and Graphics , Software Engineering/Programming and Operating Systems
| British Library Conference Proceedings | 2023
Semi-automated extraction of information from large-scale historical maps
| TIBKAT | 2023
Georeferencing of Historical Maps Using Back Propagation Artificial Neural Network
| British Library Online Contents | 2012