Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 399–406, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-399-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 399–406, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-399-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

FROM 2D TO 3D SUPERVISED SEGMENTATION AND CLASSIFICATION FOR CULTURAL HERITAGE APPLICATIONS

E. Grilli1,2, D. Dininno1,3, G. Petrucci4,5,6, and F. Remondino1 E. Grilli et al.
  • 13D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy
  • 2Alma Mater Studiorum, Bologna, Italy
  • 3Scienze dell antichità e archeologia, University of Pisa, Italy
  • 4Data and Knowledge Management (DKM) unit & Process and Data Intelligence (PDI) unit, Bruno Kessler Foundation (FBK), Trento, Italy
  • 5University of Trento, Italy
  • 6Google

Keywords: Classification, Segmentation, Cultural Heritage, Machine Learning, Random Forest

Abstract. The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials.