Segmentation of 3D Point Clouds of Heritage Buildings Using Edge Detection and Supervoxel-Based Topology

Author:

Salamanca Santiago1ORCID,Merchán Pilar1ORCID,Espacio Alejandro1ORCID,Pérez Emiliano2ORCID,Merchán María José3ORCID

Affiliation:

1. Departamento de Ingeniería Eléctrica, Electrónica y Automática, Escuela de Ingenierías Industriales, Universidad de Extremadura, Avda. Elvas s/n, 06006 Badajoz, Spain

2. Departamento de Expresión Gráfica, Escuela de Ingenierías Industriales, Universidad de Extremadura, Avda. Elvas s/n, 06006 Badajoz, Spain

3. Departamento de Didáctica de las Ciencias Sociales, Lengua y Literatura, Facultad de Educación y Psicología, Universidad de Extremadura, Avda. Elvas s/n, 06006 Badajoz, Spain

Abstract

This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings.

Funder

Agencia Estatal de Investigación

Consejería de Economía, Ciencia y Agenda Digital

Publisher

MDPI AG

Reference47 articles.

1. Review of built heritage modelling: Integration of HBIM and other information techniques;Yang;J. Cult. Herit.,2020

2. Kaufman, A.E. (2023, August 10). Voxels as a Computational Representation of Geometry. Available online: https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.8917.

3. Voxel-based representation of 3D point clouds: Methods, applications, and its potential use in the construction industry;Xu;Autom. Constr.,2021

4. From 3D point clouds to HBIM: Application of Artificial Intelligence in Cultural Heritage;Cotella;Autom. Constr.,2023

5. Linking Points with Labels in 3D: A Review of Point Cloud Semantic Segmentation;Xie;IEEE Geosci. Remote Sens. Mag.,2020

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