The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-2/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2/W1-2022, 429–434, 2022
https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-429-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2/W1-2022, 429–434, 2022
https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-429-2022

  25 Feb 2022

25 Feb 2022

AN IMAGE-BASED DEEP LEARNING WORKFLOW FOR 3D HERITAGE POINT CLOUD SEMANTIC SEGMENTATION

E. Pellis1,3, A. Murtiyoso2, A. Masiero1, G. Tucci1, M. Betti1, and P. Grussenmeyer3 E. Pellis et al.
  • 1Department of Civil and Environmental Engineering (DICEA), University of Florence, 50139 Florence, Italy
  • 2Forest Resources Management Group, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, Switzerland
  • 3Université de Strasbourg, INSA Strasbourg, CNRS, ICube Laboratory UMR 7357, Photogrammetry and Geomatics Group, 67000 Strasbourg, France

Keywords: 3D Point Cloud, Deep Learning, Semantic Segmentation, Cultural Heritage

Abstract. The interest in high-resolution semantic 3D models of historical buildings continuously increased during the last decade, thanks to their utility in protection, conservation and restoration of cultural heritage sites. The current generation of surveying tools allows the quick collection of large and detailed amount of data: such data ensure accurate spatial representations of the buildings, but their employment in the creation of informative semantic 3D models is still a challenging task, and it currently still requires manual time-consuming intervention by expert operators. Hence, increasing the level of automation, for instance developing an automatic semantic segmentation procedure enabling machine scene understanding and comprehension, can represent a dramatic improvement in the overall processing procedure. In accordance with this observation, this paper aims at presenting a new workflow for the automatic semantic segmentation of 3D point clouds based on a multi-view approach. Two steps compose this workflow: first, neural network-based semantic segmentation is performed on building images. Then, image labelling is back-projected, through the use of masked images, on the 3D space by exploiting photogrammetry and dense image matching principles. The obtained results are quite promising, with a good performance in the image segmentation, and a remarkable potential in the 3D reconstruction procedure.