TY - GEN
T1 - AUTOMATIC CREATION AND ENRICHMENT OF 3D MODELS FOR PIPE SYSTEMS BY CO-REGISTRATION OF LASER-SCANNED POINT CLOUDS AND PHOTOS
AU - Pan, Yuandong
AU - Noichl, Florian
AU - Braun, Alexander
AU - Borrmann, André
AU - Brilakis, Ioannis
N1 - Publisher Copyright:
© European Council on Computing in Construction (EC3).
PY - 2022
Y1 - 2022
N2 - An information-rich digital model for pipe systems is valuable for facility management and maintenance. Pipe systems in existing facilities can be captured for example using laser scanning equipment or cameras, providing point clouds or images. While these two data sources can provide diverse information, it is not straightforward to register one with the other. In this paper, we propose a novel approach to automatically create and enrich geometric models for pipe systems by co-registering laser-scanned point clouds and photos. Data from two separate sources are collected to test our method. Subsequently, a photogrammetric point cloud is reconstructed to establish a mapping between all 2D images and the laser-scanned 3D point cloud. State-of-the-art computer vision methods are applied to enrich the raw 2D and 3D datasets. Finally, we use the mapping to merge the processed datasets into one combined, information-rich model.
AB - An information-rich digital model for pipe systems is valuable for facility management and maintenance. Pipe systems in existing facilities can be captured for example using laser scanning equipment or cameras, providing point clouds or images. While these two data sources can provide diverse information, it is not straightforward to register one with the other. In this paper, we propose a novel approach to automatically create and enrich geometric models for pipe systems by co-registering laser-scanned point clouds and photos. Data from two separate sources are collected to test our method. Subsequently, a photogrammetric point cloud is reconstructed to establish a mapping between all 2D images and the laser-scanned 3D point cloud. State-of-the-art computer vision methods are applied to enrich the raw 2D and 3D datasets. Finally, we use the mapping to merge the processed datasets into one combined, information-rich model.
UR - http://www.scopus.com/inward/record.url?scp=85177202154&partnerID=8YFLogxK
U2 - 10.35490/EC3.2022.181
DO - 10.35490/EC3.2022.181
M3 - Conference contribution
AN - SCOPUS:85177202154
SN - 9788875902261
T3 - Proceedings of the European Conference on Computing in Construction
SP - 308
EP - 315
BT - Proceedings of the 2022 European Conference on Computing in Construction
A2 - Tagliabue, Lavinia Chiara
A2 - Hall, Daniel M.
A2 - Soman, Ranjith
A2 - Chassiakos, Athanasios
A2 - Nikolic, Dragana
PB - European Council on Computing in Construction (EC3)
T2 - European Conference on Computing in Construction, EC3 2022
Y2 - 24 July 2022 through 26 July 2022
ER -