The creation of as-built BIM models sees in the scan to BIM modeling one of the most time-consuming activities. Scan to BIM modeling refers to the creation of BIM objects from information derived from point clouds acquired through laser scans or photogrammetric techniques. Numerous studies have been conducted in recent years to identify automation or semi-automation procedures for the scan to BIM modeling process, which consists of different aspects: the recognition of objects within the scene, the modeling of their geometry and the recognition of the relationships between them. The present work aims to analyze actual trends in the automation of scan to BIM activities, highlighting the most used approaches and methodologies currently presented in order to provide a key to un-derstanding the development of a theme still at the dawn of its expression.
Automatic Recognition Through Deep Learning of Standard Forms in Executive Projects
In this paper is presented a possible methodology for automation through the use of deep learning of BIM modeling starting from different types of formats, such as digital processing of paper documents and CAD formats. The work is configured as a proof of concept of a possible contribution that a technique currently scarcely used in the architectural field such as deep learning can bring to the design, in particular in the realization of the information model, which today represents one of the most consuming–time activities.