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Article

Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

Photogrammetry and Geomatics Group, ICube Laboratory UMR 7357, INSA Strasbourg, University of Strasbourg, F-67000 Strasbourg, France
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This paper is an extended version of our paper published in the 8th International Workshop 3D-ARCH, 6–8 February 2019, Bergamo, Italy as well as another paper presented in the 27th International CIPA Symposium, 1–5 September 2019, Ávila, Spain.
Received: 14 February 2020 / Revised: 3 April 2020 / Accepted: 7 April 2020 / Published: 11 April 2020
(This article belongs to the Special Issue Sensors for Cultural Heritage Monitoring)
3D heritage documentation has seen a surge in the past decade due to developments in reality-based 3D recording techniques. Several methods such as photogrammetry and laser scanning are becoming ubiquitous amongst architects, archaeologists, surveyors, and conservators. The main result of these methods is a 3D representation of the object in the form of point clouds. However, a solely geometric point cloud is often insufficient for further analysis, monitoring, and model predicting of the heritage object. The semantic annotation of point clouds remains an interesting research topic since traditionally it requires manual labeling and therefore a lot of time and resources. This paper proposes an automated pipeline to segment and classify multi-scalar point clouds in the case of heritage object. This is done in order to perform multi-level segmentation from the scale of a historical neighborhood up until that of architectural elements, specifically pillars and beams. The proposed workflow involves an algorithmic approach in the form of a toolbox which includes various functions covering the semantic segmentation of large point clouds into smaller, more manageable and semantically labeled clusters. The first part of the workflow will explain the segmentation and semantic labeling of heritage complexes into individual buildings, while a second part will discuss the use of the same toolbox to segment the resulting buildings further into architectural elements. The toolbox was tested on several historical buildings and showed promising results. The ultimate intention of the project is to help the manual point cloud labeling, especially when confronted with the large training data requirements of machine learning-based algorithms. View Full-Text
Keywords: heritage; 3D documentation; point cloud; automation; segmentation; classification; GIS heritage; 3D documentation; point cloud; automation; segmentation; classification; GIS
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MDPI and ACS Style

Murtiyoso, A.; Grussenmeyer, P. Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements. Sensors 2020, 20, 2161. https://0-doi-org.brum.beds.ac.uk/10.3390/s20082161

AMA Style

Murtiyoso A, Grussenmeyer P. Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements. Sensors. 2020; 20(8):2161. https://0-doi-org.brum.beds.ac.uk/10.3390/s20082161

Chicago/Turabian Style

Murtiyoso, Arnadi, and Pierre Grussenmeyer. 2020. "Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements" Sensors 20, no. 8: 2161. https://0-doi-org.brum.beds.ac.uk/10.3390/s20082161

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