Abstract
In recent years, the use of 3D models in cultural and archaeological heritage for documentation and dissemination purposes is increasing. The association of heterogeneous information to 3D data by means of automated segmentation and classification methods can help to characterize, describe and better interpret the object under study. Indeed, the high complexity of 3D data along with the large diversity of heritage assets themselves have constituted segmentation and classification methods as currently active research topics. Although machine learning methods brought great progress in this respect, few advances have been developed in relation to cultural heritage 3D data. Starting from the existing literature, this paper aims to develop, explore and validate reliable and efficient automated procedures for the classification of 3D data (point clouds or polygonal mesh models) of heritage scenarios. In more detail, the proposed solution works on 2D data (“texture-based” approach) or directly on the 3D data (“geometry-based approach) with supervised or unsupervised machine learning strategies. The method was applied and validated on four different archaeological/architectural scenarios. Experimental results demonstrate that the proposed approach is reliable and replicable and it is effective for restoration and documentation purposes, providing metric information e.g. of damaged areas to be restored.
Highlights
The generation of 3D data of heritage sites or monuments, being point clouds or polygonal models, is altering the approach that cultural heritage specialists use for the analysis, interpretation, communication and valorization of such historical information
This paper presents a pipeline to classify heritage data, either working on the texture or the directly on presents the 3D geometry, on heritage the needsdata, andeither scopeworking of the classification
This paper presents a pipeline to classify 3D heritage data, either working on the texture or directly on the 3D geometry, depending on the needs and scope of the classification
Summary
The generation of 3D data of heritage sites or monuments, being point clouds or polygonal models, is altering the approach that cultural heritage specialists use for the analysis, interpretation, communication and valorization of such historical information. The management of architectural heritage information is considered crucial for a better understanding of the heritage data as well as for the development of targeted conservation policies and actions. An efficient information management strategy should take into consideration three main concepts: segmentation, structuring the hierarchical relationships and semantic enrichment [1]. The demand for automatic model analysis and understanding is continuously increasing. Recent years have witnessed significant progress in automatic procedures for segmentation and classification of point clouds or meshes [2,3,4]. There are multiple studies related to the segmentation topic, mainly driven by specific needs provided by the field of application (Building Information Modeling (BIM) [5], heritage documentation and preservation [6], robotics [7] autonomous driving [8], urban planning [9], etc.)
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