Aug 2026· Applied Sciences· Vol 16, pp. 8248· 0 citations· 27 references
TL;DR
The results demonstrate the potential of hybrid AI and geometric approaches to improve the efficiency, repeatability, and reliability of Scan-to-BIM processes for historical masonry bridge heritage and show that the geometric quality of the HBIM model depends primarily on the density, spatial distribution and completeness of the structural points, rather than on their total number.
Abstract
This paper shows an AI-assisted workflow supported by a Large Language Model for the geometric and informative reconstruction of masonry arch bridges from multi-source input data. The proposed methodology combines point cloud preprocessing, vegetation filtering, AI-based or semi-automatic segmentation, geometric feature extraction, 3D mesh generation, and parametric Heritage Building Information Modeling integration. The study specifically aims to examine how the quantity and type of input information influence the reconstruction process and the reliability of the resulting HBIM model. The input data considered include Point Clouds (PC), photographic images, and descriptive information related to the bridge geometry, construction features, and visible architectural components. Particular attention is given to the initial geometric characteristics of the point cloud, including its point density, spatial distribution, level of completeness, and overall number of points, to assess how point cloud numerosity affects the accuracy and level of detail of the reconstructed geometry. Starting from a dense 3D survey, the workflow identifies and reconstructs the main architectural and structural components of a masonry arch bridge, including the arch, intrados, parapets, masonry walls, roadway surface, abutments, and cutwaters. The extracted geometry is converted into a clean 3D mesh and subsequently structured into parametric HBIM objects suitable for documentation, conservation, structural assessment, and future monitoring activities. A Cloud-to-Mesh comparison is performed to evaluate the geometric accuracy of the reconstructed model with respect to the original point cloud. The results demonstrate the potential of hybrid AI and geometric approaches to improve the efficiency, repeatability, and reliability of Scan-to-BIM processes for historical masonry bridge heritage. Furthermore, they show that the geometric quality of the HBIM model depends primarily on the density, spatial distribution and completeness of the structural points, rather than on their total number. Well-distributed point clouds, in fact, allow for more reliable reconstructions than larger datasets characterised by uneven coverage.
Abstract. This paper presents a comprehensive methodology for the automated semantic segmentation and 3D reconstruction of industrial building elements, including roof panels, floor, rafters, purlins, and columns, from unstructured point clouds. The proposed approach integrates orientation-based filtering, projection onto characteristic planes, morphological analysis, and optimization-based I-profile fitting to generate accurate 3D models. The workflow begins with point cloud preprocessing, where the data are aligned with the building axes and cleaned of outliers, followed by subdivision into two subsets based on local surface orientation. Binary projections are then processed to extract element contours, while roof slopes and panel inclinations are automatically estimated to guide the reconstruction of rafters and purlins. The method was validated on a real-case study of 930 m² industrial warehouse scanned with a mobile laser scanner, resulting in a raw dataset of seven million points. The segmentation achieved F1-scores above 0.90 for floors, roof panels, rafters, and columns, and 0.75 for purlins. Profile fitting yielded an average width error of 3.8%, confirming the robustness and reliability of the reconstruction across diverse structural components.
P. González-Cabaleiro, M. Albadri, Antonio Fernández et al.· The International Archives o...· 0 citations
Component-based inspection and maintenance of steel girder bridges increasingly benefit from three-dimensional as-is structural models that explicitly represent individual members and inspection elements. However, most existing bridges lack digital structural models, and generating such models from scanned point clouds remains a nontrivial task due to occlusions, missing data, and the absence of design drawings. This study proposes a heuristic method for constructing lightweight parametric models of steel girder bridges from terrestrial laser scanning (TLS) point clouds. The proposed method integrates image-based neighborhood search, point-based dimensionality analysis using principal component analysis, and line-based and plane-based RANSAC to extract and reconstruct major structural components, including deck slabs, main girders, and cross beams. By exploiting domain knowledge and the geometric characteristics of steel girder bridge superstructures, the method enables component-wise segmentation and reconstruction without relying on design drawings or prior structural models. The proposed approach was validated using real-world TLS data of an existing steel girder bridge, demonstrating stable extraction and reconstruction of major structural components. The resulting parametric models explicitly represent inspection elements and have the potential to facilitate the spatial association of inspection results, photographs, and maintenance records, thereby supporting practical bridge maintenance workflows. The applicability and limitations of the proposed method are also discussed.
Tomohiro Mizoguchi· International Journal of Aut...· 0 citations
Abstract. This paper presents a semi-automated pipeline for extracting architectural plans from terrestrial LiDAR point clouds of archaeological sites characterized by irregular geometries and significant surface degradation. The proposed workflow converts dense three-dimensional data into simplified two-dimensional representations by combining geometric alignment, expert-guided cross-section selection, feature detection using Intrinsic Shape Signatures (ISS), region-based segmentation, and polyline simplification. The method is specifically designed to handle rock-cut tomb environments, where erosion, noise, and non-planar surfaces complicate conventional Scan-to-Plan approaches. The pipeline is evaluated on six tombs from the Sheikh Said necropolis in Middle Egypt, covering a range of architectural configurations and preservation states. Results demonstrate that the method efficiently produces CAD-compatible outlines that capture the dominant structural geometry while significantly reducing manual drafting time. However, the accuracy of the generated plans depends on surface conditions, with degraded or ornamented areas introducing geometric artifacts that require expert refinement. The proposed approach provides a robust geometric baseline for archaeological documentation and highlights the potential of human-in-the-loop workflows for complex heritage environments.
Marianna Bartrick-Krana, Roberto de Lima-Hernandez, A. Vandesande et al.· The International Archives o...· 0 citations
Abstract. The 3D documentation of complex scenes—characterized by restricted spaces, irregular geometries, and poor lighting—remains a significant challenge in cultural heritage. This study proposes a rapid data acquisition methodology based on the multi-sensor fusion of Terrestrial Laser Scanning (TLS) and Spherical Photogrammetry (SP). The approach was validated in two distinct complex environments: an ancient Egyptian rock-cut tomb (QH36, Aswan, Egypt) and a natural Iberian sanctuary cave (Cueva de la Lobera, Jaén, Spain). The methodology uses TLS to establish a high-precision geometric backbone, achieving registration errors below 0.5 cm. By extracting Ground Control Points (GCPs) directly from the TLS point cloud, the reliance on traditional total station surveying was significantly reduced, enhancing fieldwork efficiency. SP was implemented to obtain realistic textures and to support geometry by using a 360-degree multi-camera with integrated LED lighting, providing full spherical coverage and high-resolution textures. Results indicate that SP is at least six times faster than conventional photogrammetry. Furthermore, the use of TLS-derived meshes enabled advanced digital masking to remove non-interest objects (e.g., archaeological equipment) from the final models. While conventional photogrammetry remains the benchmark for fine architectural details, this research demonstrates that the TLS-SP fusion is the most viable solution for the rapid, high-accuracy documentation of constrained heritage sites. This hybrid workflow ensures geometric integrity while drastically reducing acquisition times, providing a robust framework for future archaeological and conservation projects.
A. Mozas-Calvache, José Luis Pérez-García, J. M. Gómez-López et al.· The International Archives o...· 0 citations
This paper presents a pilot educational workflow that couples 3D remote sensing with heritage-driven pedagogy by engaging architecture master’s students in the documentation and digital archiving of Transylvanian cultural sites. Using terrestrial and mobile 3D scanning, students documented multiple typologies—wooden churches (Târgușor, Tioltiur), historical ensembles (Mociu, Coplean), industrial sites (1 Mai–Luduș, Vânătorilor–Luduș), and an urban street segment (Potaissa)—to generate dense point clouds that served as the basis for geometric reconstruction, semantic interpretation, and condition assessment. The study describes how the characteristics of different construction systems (timber, brick, stone, mixed structures) relate to point-cloud quality, survey coverage, and subsequent CAD/BIM drafting, with attention to the qualitative reading of minor deformations in wooden churches and of degradation patterns in masonry and industrial buildings. We also consider how artefacts in the data (noise, occlusions, registration errors) affect scene understanding and the interpretation of derived observations relevant to condition assessment and, prospectively, to monitoring. For the Tioltiur dual-sensor case, the TLS and SLAM datasets were compared through an internal CloudCompare registration check (final RMS 0.1121 on 50,000 points, fixed scale 1.0 and theoretical overlap 100%), surface-density displays (r = 0.005 for the Z+F dataset and for the GeoSLAM dataset), fitted-wall-plane readings (dip values around 89 deg. and 85 deg.) and a longitudinal section documenting roof/vault deformation. Beyond technical performance, the paper examines the self-reported formative impact on students’ digital skills and their understanding of cultural values, arguing that participation in 3D data acquisition, processing, and interpretation positions them as co-creators of a living digital archive. Pre- and post-workshop questionnaires (n = 13 each) are analysed descriptively—counts, percentages and medians with interquartile ranges—because the two instruments are unmatched and carry no shared identifier, so no paired test is applied; post-workshop self-ratings of technical competence, heritage understanding, archival awareness and collaboration were consistently high (medians 4–5), with uneven access to VR the main gap. By connecting point-cloud-based documentation workflows with heritage education, the project outlines a transferable, monitoring-ready baseline model in which 3D remote sensing supports both careful documentation and the transmission of regional identity and cultural meaning in architectural training. As an exploratory pilot with a small, self-reported sample, the study reports descriptive and qualitative findings rather than validated metric or statistical results.
A. Voinea, C. Neamțu, Virgil Pop· Remote Sensing· 0 citations
This study presents a modular AI-assisted workflow for converting single 2D interior images into textured 3D assets and for evaluating those assets when ground-truth 3D meshes are unavailable. The proposed pipeline combines object detection, instance isolation, monocular-depth estimation, image-to-3D generation, texture synthesis, mesh export, and cloud-based execution to support early-stage interior-design and real-estate visualization tasks. A reference-free validation protocol is introduced, based on rendered multi-view comparisons, silhouette Intersection-over-Union, automated captioning, and multimodal embedding similarity, and is complemented by a composite validation framework that benchmarks reconstructed scenes against 200 panoramic indoor scenes from the Structured3D dataset using Hungarian-matched placement, size, recall, and relative-distance metrics. The workflow was implemented and tested using contemporary computer-vision and generative 3D components, with Hunyuan3D 2.0 used as the main reconstruction model. Proof-of-concept experiments on a representative corpus of 178 synthetically generated single-object images spanning a range of interior furniture categories show comparable silhouette IoU for textured and non-textured outputs and indicate that texture-preserving renderings improve visual and semantic similarity scores across CLIP-based evaluations. The 200-scene dataset evaluation reveals stable spatial localization (placement error ≈ 1.18 m, relative-distance error ≈ 0.54 m) alongside systematic over-prediction and size-calibration errors. Beyond the applied pipeline, the study contributes a reference-free, ground-truth-free protocol for 3D-asset evaluation and a first quantified account of where object-centric single-image reconstruction is reliable—spatial placement—and where it is not—object scale and spurious detection—at interior-scene scale. The results demonstrate the feasibility of integrating perception, 3D reconstruction, semantic assessment, and scalable deployment into a single applied pipeline, while remaining proof-of-concept and requiring extension to larger object and scene corpora, baselines, real-photograph evaluation, and human-centered assessment before broad claims about general interior-scene reconstruction can be made.
Dan Toderici, Tiberiu-Gabriel Rodanciuc, George-Alexandru Micu et al.· Electronics· 0 citations