Evaluation of Primitive Extraction Methods from Point Clouds of Cultural Heritage Buildings

Carlos Pérez-Sinticala, Romain Janvier, Xavier Brunetaud, Sylvie Treuillet, Rafael Aguilar, Benjamín Castañeda

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

This article focuses on the development of tools for automatic recognition and segmentation of the main geometrical characteristics of heritage buildings (walls, towers, roofs, slopes, etc.) to simplify a 3D point cloud into simpler model based on geometric primitives. After evaluation of well known techniques for point cloud segmentation, an hybrid method based on region growing algorithm and primitive fitting by Sample Consensus appears as the most successful. Then, a refinement process is applied by grouping close-by points into voxels and assigning them to the closest primitive. The final algorithm is tested in the front wall of the castle of Chambord, France showing a 94.40% coincidence between the geometric primitives found and manual ground truth. This algorithm might prove useful for obtaining simpler models of cultural heritage structures, which can be used for storage, manipulation and even other types of analysis such as finite element models.

Original languageEnglish
Title of host publicationRILEM Bookseries
PublisherSpringer Netherlands
Pages2332-2341
Number of pages10
DOIs
StatePublished - 2019

Publication series

NameRILEM Bookseries
Volume18
ISSN (Print)2211-0844
ISSN (Electronic)2211-0852

Keywords

  • 3D segmentation
  • Cultural heritage
  • Geometric primitive
  • Region growing
  • Voxelization

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