Classification of 3D Digital Heritage

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.

Geometric Features Analysis for the Classification of Cultural Heritage Point Clouds

“…The ever-growing use in the last years of 3D models in different applications has led 3D data classification to become a very active research topic. The possibility to automatically group big data into multiple homogeneous regions with similar properties (segmentation) and attribute labels to them (classification or semantic segmentation), have become of primary importance in various applications and fields such as robotics (Maturana et al, 2015), autonomous driving (Wang et al, 2017), urban planning (Xu et al, 2014), heritage (Grilli and Remondino, 2019), geospatial (Özdemir and Remondino, 2018), etc. Different approaches were proposed in the literature (Grilli et al, 2017), but only recently significant progress has come out in automatic procedures thanks to the advent of Machine Learning approaches (Hackel et al, 2017;Weinmann et al, 2017;Wang et al, 2019).…”

Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

“…Despite some works that attempt at classifying DCH images by employing different kinds of techniques [33][34][35][36] already exist, there are still few researches who seek to directly exploit the Point Clouds of CH for semantic classification or segmentation through ML [37] or DL techniques. One of them is [38], where a segmentation of 3D models of historical buildings is proposed for FEA analysis, starting from Point Clouds and meshes.…”

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

“…As deep learning is a well-established technique in the realm of 2D image recognition, one way to perform point cloud classification is to apply deep learning on 2D images created from point cloud color (orthophotos, UV textures, etc.) [38]. The technique is also often used to perform the segmentation and classification of point cloud generated by aerial platforms (aerial photogrammetry, ALS) as it enables the reduction of the (usually more complex) 3D point cloud into a 2.5D problem [39].…”