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

Article dans une revue avec comité de lecture
Author
ccCROCE, Valeria
261638 Modèles et simulations pour l'Architecture et le Patrimoine [MAP]
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
CAROTI, Gabriella
DE LUCA, Livio
261638 Modèles et simulations pour l'Architecture et le Patrimoine [MAP]
JACQUOT, Kévin
261638 Modèles et simulations pour l'Architecture et le Patrimoine [MAP]
PIEMONTE, Andrea
VÉRON, Philippe
58355 École Nationale Supérieure des Arts et Métiers [ENSAM]
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]

URI
http://hdl.handle.net/10985/19975
DOI
10.3390/rs13030461
Date
2021
Journal
Remote Sensing

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.

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