GEOMETRIC FEATURES ANALYSIS FOR THE CLASSIFICATION OF CULTURAL HERITAGE POINT CLOUDS

29 citations as recorded by crossref.

1. Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation F. Matrone et al. https://doi.org/10.3390/ijgi9090535
2. Two-Dimensional Shape Analysis of Complex Geometry Based on Photogrammetric Models of Iconostases I. Đurić et al. https://doi.org/10.3390/app11157042
3. Beyond scenic views: an integrative review of visual research on heritage landscapes—themes, methods, and typologies across scales Y. Peng et al. https://doi.org/10.1016/j.landurbplan.2026.105742
4. Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds F. Matrone & M. Martini https://doi.org/10.4995/var.2021.15318
5. Application of modern survey and cartographic methods for documenting and presenting existing and historical workings of the UNESCO heritage site: Silver mine in Tarnowskie Góry T. Dąbrowa et al. https://doi.org/10.1051/e3sconf/202456701027
6. 3D Feature Engineering for Deep Semantic Segmentation of Indoor Point Clouds M. Mérizette et al. https://doi.org/10.1111/phor.70047
7. Point cloud classification and part segmentation of steel structure elements H. Daif & M. Marzouk https://doi.org/10.1007/s00521-024-10733-x
8. Multi-scale neighborhood selection network for the Great Wall point cloud segmentation L. Wang et al. https://doi.org/10.1038/s40494-025-01545-w
9. H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction V. Croce et al. https://doi.org/10.3390/s23052497
10. A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification S. Teruggi et al. https://doi.org/10.3390/rs12162598
11. Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners M. Ali et al. https://doi.org/10.3390/rs16061021
12. An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios E. Özdemir et al. https://doi.org/10.3390/rs13101985
13. Machine Learning for the Semi-Automatic 3D Decay Segmentation and Mapping of Heritage Assets R. Galantucci et al. https://doi.org/10.1080/15583058.2023.2287152
14. A Holistic Solution for Supporting the Diagnosis of Historic Constructions from 3D Point Clouds L. Sánchez-Aparicio et al. https://doi.org/10.3390/rs17122018
15. Fast adaptive multimodal feature registration (FAMFR): an effective high-resolution point clouds registration workflow for cultural heritage interiors P. Foryś et al. https://doi.org/10.1186/s40494-023-01018-y
16. Multiclass semantic segmentation for digitisation of movable heritage using deep learning techniques G. Patrucco & F. Setragno https://doi.org/10.4995/var.2021.15329
17. Evaluation of Automated Machine Learning and Model Transferability Approaches for Automating the Diagnosis of Historic Constructions. A Case Study in the Almeida Fortress (Portugal) P. Sanz-Honrado et al. https://doi.org/10.1080/15583058.2025.2580386
18. Recent Advances and Challenges in the Field of Mechatronic Systems and Digital Technologies for Structural Surveying, Inspection and Assessment of Heritage Masonry Structures J. Ortega et al. https://doi.org/10.67908/rrbe.e0004
19. Semi-automatic classification of digital heritage on the Aïoli open source 2D/3D annotation platform via machine learning and deep learning V. Croce et al. https://doi.org/10.1016/j.culher.2023.05.017
20. Pixel-level block classification and crack detection from 3D reconstruction models of masonry structures using convolutional neural networks D. Loverdos & V. Sarhosis https://doi.org/10.1016/j.engstruct.2024.118113
21. An enhanced descriptor extraction algorithm for power line detection from point clouds D. Shokri et al. https://doi.org/10.1111/1745-5871.12604
22. From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning V. Croce et al. https://doi.org/10.3390/rs13030461
23. Web-based platform to collect, share and manage technical data of historical systemic architectures: the Telegraphic Towers along the Madrid-Valencia path M. Lasorella et al. https://doi.org/10.4995/var.2024.20341
24. An Analysis of Research Trends for Using Artificial Intelligence in Cultural Heritage F. Gîrbacia https://doi.org/10.3390/electronics13183738
25. Networking Historic Environmental Standards to Address Modern Challenges for Sustainable Conservation in HBIM O. Prizeman et al. https://doi.org/10.3390/app10041283
26. Unsupervised Dual Transformer Learning for 3-D Textured Surface Segmentation I. Ganapathi et al. https://doi.org/10.1109/TNNLS.2024.3365515
27. Metrological Analysis with Covariance Features of Micro-Channels Fabricated with a Femtosecond Laser M. Verdi et al. https://doi.org/10.3390/metrology4030024
28. From Symmetry to Semantics: Improving Heritage Point Cloud Classification with a Geometry-Aware, Uniclass-Informed Taxonomy for Random Forest Implementation in Automated HBIM Modelling A. Gil & Y. Arayici https://doi.org/10.3390/sym17101635
29. To save from oblivion: Comparative analysis of remote sensing means of documenting forgotten architectural treasures – Zagórz Monastery complex, Poland P. Lewińska et al. https://doi.org/10.1016/j.measurement.2021.110447