Semi-automatic roof modelling from indoor laser-acquired data

Roof modelling provides useful information for energy analysis, but the methodologies traditionally applied are based on data acquired through aerial vehicles. This requirement makes necessary two data acquisition campaigns: one from indoors and another from outdoors. However, most energy studies can be performed using regularized and simplified models where most of the information of the exhaustive acquisitions is not used. Therefore, this paper proposes a semi-automatic procedure for the 3D modelling of roofs using indoor point clouds, reducing the acquisition campaigns to the indoors campaign. The methodology is based on the hypothesis that surfaces have no thickness, which makes the algorithm especially useful in industrial environments where there are no false ceilings and therefore, the contribution of the roof in the energy behaviour of the building is more important. The methodology is tested on six different scenarios, obtaining their regularized models with relative errors lower than 2% in ideal conditions.