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A Computer Vision Approach for Building Facade Component Segmentation on 3D Point Cloud Models Reconstructed by Aerial Images

Hou, Yu; Mayer, Zoe; Li, Zhaoyang; Rebekka Volk; Lucio Soibelman


Segmenting windows and doors on 3D point cloud models allows for heat loss audits around these areas. Researchers have collected aerial images to reconstruct 3D models for large districts, but easily accessible training datasets with data acquired on ground level cannot be directly used for segmentation on 3D models reconstructed by aerial images. Additionally, building a new dataset is a time-consuming and labour-intensive process. Therefore, we propose a segmentation approach that uses open source training datasets to segment windows and doors on façade images rendered from 3D point clouds. The results show that our approach can make full use of open source
datasets to segment windows and doors, and that such trained segmentation models performs differently for different building styles. In addition, different algorithms result in various degrees of accuracy and segmentation on windows performs better than on doors.

Verlagsausgabe §
DOI: 10.5445/IR/1000136255
Veröffentlicht am 05.11.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISBN: 978-3-7983-3212-6
KITopen-ID: 1000136255
Erschienen in EG-ICE 2021 Proceedings: Workshop on Intelligent Computing in Engineering. Ed.: J. Abualdenien
Veranstaltung 28th EG-ICE International Workshop on Intelligent Computing in Engineering (2021), Berlin, Deutschland, 30.06.2021 – 02.07.2021
Auflage 1
Verlag Universitätsverlag der TU Berlin
Seiten 561–571
Externe Relationen Abstract/Volltext
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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