A growing number of research works, experiments and applications is investigating the potential at the intersection of augmented reality (AR) and the architecture, engineering, and construction (AEC) industry. Project management, project communication, collaborative design, maintenance and construction progress documentation, construction site safety, and training are some of the cases that can benefit from blending real and virtual views through mobile devices. In recent years, research also highlighted how mixed reality and building information modeling (BIM) could cooperate to provide effective communication between multiple agents and closer interaction between digital information and the building site. Nevertheless, consolidated applications in these fields are still limited, especially when compared to other areas of AR adoption. This paper presents the development of an AR-based mobile app for exploring telecommunications tower sites and interacting with a related BIM database. The project aims to provide easy-to-use tools to maintain both the physical assets and an up-to-date model. We discuss critical issues in developing a scalable and interoperable application, supporting the feasibility study of similar solutions in the AEC sector.
From semantic-aware digital models to Augmented Reality applications for Architectural Heritage conservation and restoration
The paper presents the integration of Augmented Reality and Mixed Reality tools for the built Heritage management and control, both remote and on-site, and real time interaction, starting from a preliminary set of experimentation carried out for knowledge and tourism purposes.
Within the framework of these experimentations, specific data inventories are related to the IFC model, and all these data are collected on a cloud-based platform, allowing the “dialogue” among platform and applications. Therefore, BIM integration is the first step of the procedure, considering a workflow where data capturing, digital documentation, and data modeling and aggregation are the entry level to manage applications able to give an added value in gaining the greatest technical benefit from digitization. Mapping the main features and the state of conservation is the second step, including geometric features, historical knowledge, documents and pictures related to materials, diagnostic analysis, etc.
Starting from AR applications developed on several case studies, including historical buildings, museums and a church, aimed at an immersive on-site navigation thanks to a set of additional information related to the digital model, experimentations oriented to technical uses are presented.
An extension of applications for the analysis and interpretation of architectural heritage and technical uses can be an effective support in restoration, conservation and maintenance of historic buildings, by enhancing the real world through virtual objects and creating a new mixed reality environment for technical users.
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.
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.
A Proposal of Integration of Point Cloud Semantization and VPL for Architectural Heritage Parametric Modeling
Current architectural survey processes utilize point clouds generated by laser scanning and digital photogrammetry. Increasingly, these surveys produce 3D models, particularly parametric models, in what is known as the “scan to 3D model” or “scan to BIM” process. However, the phases of analysis and classification of architectural elements, segmentation and semantization of point clouds, and semi-automatic modeling remain complex and labor-intensive and require an active role commitment of the scholar or modeler. These steps are usually performed manually, resulting in high subjectivity and low reproducibility. This paper proposes a reproducible workflow that automatically segments point clouds, identifies geometric shapes by comparing them with a library of ideal geometries, and extracts necessary points for modeling through mathematical analysis. The extracted information is then processed using a visual programming algorithm, imported into the VPL environment, and used for automated modeling. Initial results from an ongoing experiment on the automated modeling of vaults using point clouds from surveys are presented.