Semantic segmentation through Artificial Intelligence from raw point clouds to H-BIM representation

This work describes a semi automatic workflow for the 3D reconstruction of Heritage Building Information Models from raw point clouds, based on Artificial Intelligence techniques. The BIM technology applied to historical architecture has made it possible to create a virtual repository of many heterogeneous pieces of information in order to make the process of storing and collecting data on the built heritage more effective. The modelling phase of an artefact is the most complex and problematic in terms of time, as the large architectural heritage of historical buildings does not allow the use of parametric models, so that manual modelling of components is required. Current scientific research focuses on automating this phase by means of segmentation and classification methods: these are based on associating different semantic information to the products of the three dimensional surveying as point clouds or polygonal meshes. To address these problems, the proposed approach relies on: (i) the application of machine learning algorithms with a multi level and multi resolution (MLMR) method to semantically classify 3D heritage data; (ii) the use of annotated data identified by relevant features to boost the scan to BIM process for 3D digital reconstruction. The procedure is tested and evaluated on the complex case of the Church of Santa Caterina d’Alessandria in Pisa, Italy. The classification results show the reliability and reproducibility of the developed method.