Version 1
: Received: 30 May 2024 / Approved: 30 May 2024 / Online: 30 May 2024 (11:05:27 CEST)
How to cite:
Salamanca, S.; Merchán, P.; Espacio, A.; Pérez, E.; Merchán, M. J. Segmentation of 3D Point Clouds of Heritage Buildings using Edge Detection and Supervoxel-Based Topology. Preprints2024, 2024052031. https://doi.org/10.20944/preprints202405.2031.v1
Salamanca, S.; Merchán, P.; Espacio, A.; Pérez, E.; Merchán, M. J. Segmentation of 3D Point Clouds of Heritage Buildings using Edge Detection and Supervoxel-Based Topology. Preprints 2024, 2024052031. https://doi.org/10.20944/preprints202405.2031.v1
Salamanca, S.; Merchán, P.; Espacio, A.; Pérez, E.; Merchán, M. J. Segmentation of 3D Point Clouds of Heritage Buildings using Edge Detection and Supervoxel-Based Topology. Preprints2024, 2024052031. https://doi.org/10.20944/preprints202405.2031.v1
APA Style
Salamanca, S., Merchán, P., Espacio, A., Pérez, E., & Merchán, M. J. (2024). Segmentation of 3D Point Clouds of Heritage Buildings using Edge Detection and Supervoxel-Based Topology. Preprints. https://doi.org/10.20944/preprints202405.2031.v1
Chicago/Turabian Style
Salamanca, S., Emiliano Pérez and María José Merchán. 2024 "Segmentation of 3D Point Clouds of Heritage Buildings using Edge Detection and Supervoxel-Based Topology" Preprints. https://doi.org/10.20944/preprints202405.2031.v1
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 topo-logical structure generated from the supervoxelization of the 3D point clouds, which is used to make the clo-sure 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 demonstrates 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.
Keywords
Laser scanner; 3D point clouds; Segmentation; Heritage buildings; Edge detection; Supervoxels
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
