ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 121–128, 2021
https://doi.org/10.5194/isprs-annals-V-2-2021-121-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 121–128, 2021
https://doi.org/10.5194/isprs-annals-V-2-2021-121-2021

  17 Jun 2021

17 Jun 2021

TESSERAE3D: A BENCHMARK FOR TESSERAE SEMANTIC SEGMENTATION IN 3D POINT CLOUDS

A. Kharroubi1, L. Van Wersch2, R. Billen1, and F. Poux1 A. Kharroubi et al.
  • 1Geomatics Unit, UR SPHERES, University of Liège, 4000 Liège, Belgium
  • 2UR Art, Archéologie, Patrimoine, University of Liège, 4000 Liège, Belgium

Keywords: 3D Point Cloud, Tesserae, Semantic Segmentation, Dataset, Machine Learning

Abstract. 3D point cloud of mosaic tesserae is used by heritage researchers, restorers and archaeologists for digital investigations. Information extraction, pattern analysis and semantic assignment are necessary to complement the geometric information. Automated processes that can speed up the task are highly sought after, especially new supervised approaches. However, the availability of labelled data necessary for training supervised learning models is a significant constraint. This paper introduces Tesserae3D, a 3D point cloud benchmark dataset for training and evaluating machine learning models, applied to mosaic tesserae segmentation. It is a publicly available, very high density and coloured dataset, accompanied by a standard multi-class semantic segmentation baseline. It consists of about 502 million points and contains 11 semantic classes covering a wide range of tesserae types. We propose a semantic segmentation baseline building on radiometric and covariance features fed to ensemble learning methods. The results delineate an achievable 89% F1-score and are made available under https://github.com/akharroubi/Tesserae3D, providing a simple interface to improve the score based on feedback from the research community.