This research is realised in the framework of a project recently funded as part of the PNRR (National Recovery and Resilience Plan) in the Accessibility sector. The working team has been established in the framework of the scientific agreement between the Museum of Villa della Regina in Turin, the Department of Architecture and Design at Politecnico di Torino, and the Department of History, Drawing and Restoration of Architecture at Sapienza Università di Roma, and includes knowledge from art history, digital surveying, 3D modelling, and digital solutions for cultural heritage. The research involves the reconstructive 3D modelling of Piffetti’s Library, once placed in the cabinet toward midnight and west inside the Villa della Regina and today in the Palazzo del Quirinale, and its interactive visualisation through augmented reality (AR) and virtual reality (VR) aimed at accessibility.
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.
Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure
The reconstruction of 3D geometries starting from reality-based data is challenging and timeconsuming due to the difficulties involved in modeling existing structures and the complex nature of built heritage. This paper presents a methodological approach for the automated segmentation and classification of surveying outputs to improve the interpretation and building information modeling from laser scanning and photogrammetric data. The research focused on the surveying of reticular, space grid structures of the late 19th–20th–21st centuries, as part of our architectural heritage, which might require monitoring maintenance activities, and relied on artificial intelligence (machine learning and deep learning) for: (i) the classification of 3D architectural components at multiple levels of detail and (ii) automated masking in standard photogrammetric processing. Focusing on the case study of the grid structure in steel named La Vela in Bologna, the work raises many critical issues in space grid structures in terms of data accuracy, geometric and spatial complexity, semantic classification, and component recognition.
Visual Programming for a Machine Semi-Automatic Process of HBIM Models Geometric Evaluation
The topic of the relationship between digital restitutive model and measurement can find important development possibilities in machine procedures, in particular in the Historic Building Information Modeling (HBIM) field. In fact, BIM uses parameterized and pre-defined objects in special 3D libraries articulated according to the architectural components, not corresponding to ideal configurations. Moreover, BIM platforms are limited in modeling deformations, damages, and degradations. The paper investigates the advantages of using visual programming to increase the possibilities given by the BIM software, born for new buildings, by carrying out a semi-automatic assessment of the geometric reliability directly in the BIM environment. In particular, the algorithm compares model’s shapes with the cast of the artifact given by the point cloud, and declares it, by automatically filling a dedicated reliability parameter linked to the BIM model element.