Hbim Modelling Process From 3d Point Clouds by Applying Artificial Intelligence Algorithms in Cultural Heritage

Abstract

In the context of Cultural Heritage (CH), the widespread adoption of 3D point cloud technology, coupled with Artificial Intelligence (AI) algorithms, plays a pivotal role. These technologies facilitate the creation of as-built models by integrating Building Information Modelling (BIM) strategies, enhancing collaboration within the Architecture, Engineering, and Construction (AEC) sector. Leveraging computer vision, robotics, and remote sensing, 3D points clouds provide rich data. However, manual segmentation and classification are labor-intensive and error prone. Consequently, researchers increasingly turn to machine learning (ML) and deep learning (DL) techniques for automating these tasks. The transition from manual reconstruction to automated procedures is crucial. Despite progress, gaps remain, particularly in incorporating 3D point cloud segmentation into Historical Building Information Modelling (HBIM). The lack of conclusive evidence regarding automated derivation of parametric attributes from segmentation outcomes underscores the need for further exploration. Addressing this gap is essential for cultural asset documentation, conservation, and upkeep. By automating the segmentation and classification of 3D point clouds, efficient communication via a shared database becomes feasible. The article aims to review studies on semantically parsing and classifying 3D point clouds using AI algorithms, particularly within complex cultural heritage geometries, shedding light on potential benefits and barriers.