Recently we assist to an increasing availability of HBIM models rich in geometric and informative terms. Instead, there is still a lack of researches implementing dedicated libraries, based on parametric intelligence and semantically aware, related to the architectural heritage. Additional challenges became from their portability in non-desktop environment (such as VR). The research article demonstrates the validity of a workflow applied to the architectural heritage, which starting from the semantic modeling reaches the visualization in a virtual reality environment, passing through the necessary phases of export, data migration and management. The three-dimensional modeling of the classical Doric order takes place in the BIM work environment and is configured as a necessary starting point for the implementation of data, parametric intelligences and definition of ontologies that exclusively qualify the model. The study also enables an effective method for data migration from the BIM model to databases integrated into VR technologies for AH. Furthermore, the process intends to propose a methodology, applicable in a return path, suited to the achievement of an appropriate data enrichment of each model and to the possibility of interaction in VR environment with the model.

Historical heritage is demanding robust pipelines for obtaining Heritage Building Information Modeling models that are fully interoperable and rich in their informative content. The definition of efficient Scan-to-BIM workflows represent a very important step toward a more efficient management of the historical real estate, as creating structured three-dimensional (3D) models from point clouds is complex and time-consuming. In this scenario, semantic segmentation of 3D Point Clouds is gaining more and more attention, since it might help to automatically recognize historical architectural elements. The way paved by recent Deep Learning approaches proved to provide reliable and affordable degrees of automation in other contexts, as road scenes understanding. However, semantic segmentation is particularly challenging in historical and classical architecture, due to the shapes complexity and the limited repeatability of elements across different buildings, which makes it difficult to define common patterns within the same class of elements. Furthermore, as Deep Learning models requires a considerably large amount of annotated data to be trained and tuned to properly handle unseen scenes, the lack of (big) publicly available annotated point clouds in the historical building domain is a huge problem, which in fact blocks the research in this direction. However, creating a critical mass of annotated point clouds by manual annotation is very time-consuming and impractical. To tackle this issue, in this work we explore the idea of leveraging synthetic point cloud data to train Deep Learning models to perform semantic segmentation of point clouds obtained via Terrestrial Laser Scanning. The aim is to provide a first assessment of the use of synthetic data to drive Deep Learning--based semantic segmentation in the context of historical buildings. To achieve this purpose, we present an improved version of the Dynamic Graph CNN (DGCNN) named RadDGCNN. The main improvement consists on exploiting the radius distance. In our experiments, we evaluate the trained models on synthetic dataset (publicly available) about two different historical buildings: the Ducal Palace in Urbino, Italy, and Palazzo Ferretti in Ancona, Italy. RadDGCNN yields good results, demonstrating improved segmentation performances on the TLS real datasets.

This study focuses on modeling the fourth dimension of historic architectures with an HBIM approach and special regard to stratigraphic analysis. The goal is to push the limits of current technology to understand the history of buildings, with impacts on protecting their authenticity; it is pursued with a practitioners-oriented methodology able to make aware models of their phases. The target audience are experts in the field of heritage conservation, while the outcome is to support long-term strategies for the sustainable management of heritage. Contents follow this structure: (1) Introduction: this section frames the benefits of affirming heritage’s physical authenticity and managing risks; it clarifies assumptions and the research aim; (2) State of the Art: this highlights the topic relevance, which is not yet fully resolved, focusing on semantics, critical-interpretative data control, and on the automation of some crucial results; (3) Materials and Methods: this describes the integrated workflow, including the photogrammetric acquisition, modeling, and data enrichment, the semi-automatic Harris matrix construction, and the optimization of laser data; (4) Results: this presents the results of modelling stratigraphic units, enriching them with information according to a semantics coherent with the conservation process, to govern the temporal relations while automating key outputs; (5) Discussion: this section refines the implemented solutions and introduce future works.

ABSTRACT:The availability of efficient HBIM workflows could represent a very important change towards a more efficient management of the historical real estate. The present work shows how to obtain accurate and reliable information of heritage buildings through reality capture and 3D modelling to support restoration purposes or knowledge-based applications. Two cases studies metaphorically joint Italy with Argentina.

<p><strong>Abstract.</strong> Italian Cultural Heritage is rich in fascinating Underground Heritage (UH) to be protected and preserved because of its fragility and historical importance. An accurate and high-resolution 3D model is essential to reach an appropriate level of knowledge to safeguard caves but there are several obstacles to face. Underground data acquisition and following elaborations are problematic due to environmental conditions such as lack of homogeneous light sources, highly absorbing and unstable surfaces, narrow spaces and complex geometry. For these reasons, the integration of different techniques is mandatory to achieve a valid final product that could be an important basis for consolidation, preservation and valorization of the UH. In this paper, an integrated survey method is tested for a realistic digital reconstruction of hypogeal spaces. In addition to outputs for experts of conservation, the creation of multimedia products for a wider audience of non-professionals users is investigated as a way to preserve UH from decay. Thanks to VR, visitors virtually walk through the underground galleries observing and interacting, making accessible also fragile environments with forbidden access due to preservation policies.</p>