A Holistic Solution for Supporting the Diagnosis of Historic Constructions from 3D Point Clouds - Luis Javier Sánchez-Aparicio; Rubén Santamaría-Maestro; Pablo Sanz-Honrado; Paula Villanueva-Llauradó; Jose Ramón Aira-Zunzunegui; Diego González-Aguilera (Remote Sensing, 2025)
This paper presents Segmentation for Diagnose (Seg4D), a holistic tool for processing 3D point clouds in the field of historical constructions. This tool incorporates state-of-the-art algorithms for the segmentation and analysis of construction systems and damage. Seg4D applies both supervised and unsupervised machine learning and deep learning methods, including the Point Transformer Neural Network for point cloud segmentation. Additionally, it facilitates the extraction of geometrical and statistical features, colour-scale conversion, noise reduction with anisotropic filters and the use of custom scripts for analysing deflections in slabs or out-of-plane movements in arches and vaults, among others. The Seg4D installer and source code are are publicly available in a GitHub repository.