From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning

Abstract

This work presents a semi-automatic approach to the 3D reconstruction of Heritage-Building Information Models from point clouds based on machine learning techniques. The use of digital information systems leveraging on three-dimensional (3D) representations in architectural heritage documentation and analysis is ever increasing. For the creation of such repositories, reality-based surveying techniques, such as photogrammetry and laser scanning, allow the fast collection of reliable digital replicas of the study objects in the form of point clouds. Besides, their output is raw and unstructured, and the transition to intelligible and semantic 3D representations is still a scarcely automated and time-consuming process requiring considerable human intervention. More refined methods for 3D data interpretation of heritage point clouds are therefore sought after. In tackling these issues, the proposed approach relies on (i) the application of machine learning techniques to semantically label 3D heritage data by identification of relevant geometric, radiometric and intensity features, and (ii) the use of the annotated data to streamline the construction of Heritage-Building Information Modeling (H-BIM) systems, where purely geometric information derived from surveying is associated with semantic descriptors on heritage documentation and management. The “Grand-Ducal Cloister” dataset, related to the emblematic case study of the Pisa Charterhouse, is discussed.

Hbim Modelling for an Historical Urban Centre

“…In opposition, the classification and the segmentation can benefit from algorithms implemented in clouds editing software GUI and large and complex datasets, such as machine and deep learning approaches (Matrone et al, 2020). The availability of intermediate annotated point clouds contributes to streamlining the workflow for semantic elements modelling toward HBIM systems within the parametric modelling workflow (Croce et al, 2021). In multiscale urban documentation, the more recent diffusion of mobile mapping systems based on multiple sensor fusion contributes providing a suitable point cloud dataset that is particularly configured to be annotated and segmented.…”

Digital Modelling and Accuracy Verification of a Complex Architectural Object Based on Photogrammetric Reconstruction

“…Properly parameterised and reconstructed, spatial data can be used for defect detection and deformation measurement [21,22]. The resulting 3D models can also be used in heritage building information modelling (HBIM) applications [23][24][25][26] and to evaluate distinctive properties of existing buildings [27]. SFM and MVS reconstructions made using VisualSFM software can monitor construction progress via comparison with BIM models [28].…”

“…Due to the widespread use of unmanned aerial vehicles (UAV) technology, there is an increasing number of studies demonstrating the potential for reconstruction both based on data acquired only with UAVs and their combination with terrestrial data [15,[37][38][39][40][41]. Increasingly sophisticated IT tools are being used [26,32,42,43] in the process of reconstructing buildings and improving the accuracy of this process. However, this precision cannot always be verified.…”

Automatic Identification and Geometrical Modeling of Steel Rivets of Historical Structures from Lidar Data

“…For the process of preservation, maintenance, and conservation of historical structures, HBIM (Historic Building Information Modeling) technology is currently gaining generalized use [1][2][3][4][5][6]. HBIM technology is based on collecting data on the building of interest by using different techniques and the posterior exploitation of this information to make up detailed 3D models.…”