Abstract
Abstract. The lack of benchmarking data for the semantic segmentation of digital heritage scenarios is hampering the development of automatic classification solutions in this field. Heritage 3D data feature complex structures and uncommon classes that prevent the simple deployment of available methods developed in other fields and for other types of data. The semantic classification of heritage 3D data would support the community in better understanding and analysing digital twins, facilitate restoration and conservation work, etc. In this paper, we present the first benchmark with millions of manually labelled 3D points belonging to heritage scenarios, realised to facilitate the development, training, testing and evaluation of machine and deep learning methods and algorithms in the heritage field. The proposed benchmark, available at http://archdataset.polito.it/, comprises datasets and classification results for better comparisons and insights into the strengths and weaknesses of different machine and deep learning approaches for heritage point cloud semantic segmentation, in addition to promoting a form of crowdsourcing to enrich the already annotated database.
Highlights
The growing ease of point cloud acquisition, especially due to the developments of automated image-based solutions, SLAM methods and laser scanning systems, has created an increasing interest of the scientific community towards the use, interpretation and direct exploitation of point clouds for many different purposes
This paper describes ArCH benchmark, conceived for 3D point cloud semantic segmentation
Some previous studies have demonstrated that Convolutional Neural Networks (CNN) methods offer reliable strategies for 3D Cultural Heritage (CH) data classification
Summary
The growing ease of point cloud acquisition, especially due to the developments of automated image-based solutions, SLAM methods and laser scanning systems, has created an increasing interest of the scientific community towards the use, interpretation and direct exploitation of point clouds for many different purposes. In the Cultural Heritage (CH) field, HBIM (Historical Building Information Modeling) has gained particular attention from experts, since it allows to manage architectural heritage data, in both geometrical and informative ways (Bruno and Roncella, 2018) As it is well-known, whether point clouds provide a needful starting point, the process of developing HBIM models is still entrusted on manual operation; experts are claimed at handling large and complex datasets, without the aid of any automatic or semi-automatic method to recognise and reshape 3D elements (Bitelli et al, 2017). The realised benchmark originates from the collaboration of different universities and research institutes (Politecnico di Torino, Università Politecnica delle Marche, FBK Trento, Italy, and INSA Strasbourg, France) It is unique as it offers, for the first time to the research community, annotated point clouds describing heritage scenes. For a more profitable use of this benchmark, aside from free download of all data, we provide public results of the submitted approaches, providing rankings about the most performing ones
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