Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Monitoring Physical Progress of Indoor Buildings Using Mobile and Terrestrial Point Clouds
Monitoring the amount of 3D physical progress of indoor buildings is crucial in demonstrating of the level of productivity and efficiency on a construction site. To monitor this type of progress, accurate 3D data such as advanced mobile and terrestrial lidar data should be collected at each step of the work process. While 3D physical progress of outdoor construction sites have been studied so far, algorithms of progress monitoring have been rarely evaluated for application on both mobile and terrestrial lidar data sets for indoor buildings. This paper aims to evaluate the performances of two progress monitoring algorithms, namely C2C and M3C2, for application on time series mobile and terrestrial lidar datasets. The evaluation is based on speed of processing and classes of magnitude of change. Better performance of M3C2 is proven by the results because it is faster than the C2C algorithm especially when both types of mobile and terrestrial lidar data sets are used. In addition, M3C2 is able to detect both classes of change, either increased or decreased during the construction progress.
Monitoring Physical Progress of Indoor Buildings Using Mobile and Terrestrial Point Clouds
Monitoring the amount of 3D physical progress of indoor buildings is crucial in demonstrating of the level of productivity and efficiency on a construction site. To monitor this type of progress, accurate 3D data such as advanced mobile and terrestrial lidar data should be collected at each step of the work process. While 3D physical progress of outdoor construction sites have been studied so far, algorithms of progress monitoring have been rarely evaluated for application on both mobile and terrestrial lidar data sets for indoor buildings. This paper aims to evaluate the performances of two progress monitoring algorithms, namely C2C and M3C2, for application on time series mobile and terrestrial lidar datasets. The evaluation is based on speed of processing and classes of magnitude of change. Better performance of M3C2 is proven by the results because it is faster than the C2C algorithm especially when both types of mobile and terrestrial lidar data sets are used. In addition, M3C2 is able to detect both classes of change, either increased or decreased during the construction progress.
Monitoring Physical Progress of Indoor Buildings Using Mobile and Terrestrial Point Clouds
Shirowzhan, Sara (Autor:in) / Sepasgozar, Samad (Autor:in) / Liu, Chang (Autor:in)
Construction Research Congress 2018 ; 2018 ; New Orleans, Louisiana
Construction Research Congress 2018 ; 602-611
29.03.2018
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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