Abstract
Abstract. Point clouds obtained via Terrestrial Laser Scanning (TLS) surveys of historical buildings are generally transformed into semantically structured 3D models with manual and time-consuming workflows. The importance of automatizing this process is widely recognized within the research community. Recently, deep neural architectures have been applied for semantic segmentation of point clouds, but few studies have evaluated them in the Cultural Heritage domain, where complex shapes and mouldings make this task challenging. In this paper, we describe our experiments with the DGCNN architecture to semantically segment historical buildings point clouds, acquired with TLS. We propose a variation of the original approach where a radius distance based technique is used instead of K-Nearest Neighbors (KNN) to represent the neighborhood of points. We show that our approach provides better results by evaluating it on two real TLS point clouds, representing two Italian historical buildings: the Ducal Palace in Urbino and the Palazzo Ferretti in Ancona.
Highlights
Built Cultural Heritage management and preservation requires the creation of accurate and rich digital representations of historical buildings
In this paper we describe our experiments with the Dynamic Graph Convolutional Neural Network (DGCNN) architecture to semantically segment historical buildings Terrestrial Laser Scanning (TLS) point clouds
We show that our approach provides better results by evaluating it on two real TLS point clouds, representing two Italian historical buildings: the Ducal Palace in Urbino and the Palazzo Ferretti in Ancona
Summary
Built Cultural Heritage management and preservation requires the creation of accurate and rich digital representations of historical buildings. The process of transforming a point cloud to BIM is referred to as Scan-to-BIM and is usually carried out manually through a careful and time-consuming work. In the case of historical buildings, it is desirable to automatically segment the 3D point cloud into specific architecture elements, referring to robust and consolidated thesaurus. The interest in this kind of task is witnessed by a number of recent research efforts, which attempt at addressing it with algorithmic workflows (Murtiyoso, Grussenmeyer, 2020a) or leveraging machine learning methods based on hand-crafted features (Grilli et al, 2019b)
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