International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1419–1426, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1419-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1419–1426, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1419-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Aug 2020

14 Aug 2020

A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION

F. Matrone1, A. Lingua1,2, R. Pierdicca3, E. S. Malinverni3, M. Paolanti4, E. Grilli5, F. Remondino5, A. Murtiyoso6, and T. Landes6 F. Matrone et al.
  • 1DIATI, Politecnico di Torino, Torino, Italy
  • 2PIC4SeR, Politecnico Interdepartmental Center for Service Robotics, Politecnico di Torino, Torino, Italy
  • 3DICEA, Università Politecnica delle Marche, Ancona, Italy
  • 4DII, Università Politecnica delle Marche, Ancona, Italy
  • 53D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy
  • 6Photogrammetry and Geomatics Group, ICube Laboratory UMR 7357, INSA Strasbourg, France

Keywords: benchmark, 3D heritage, point cloud, semantic segmentation, classification, machine learning, deep learning

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.