DEEP LEARNING FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUD

24 citations as recorded by crossref.

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3. A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification S. Teruggi et al. 10.3390/rs12162598
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11. 3D survey and virtual reconstruction of heritage. The case study of the City Council and Lonja of Alcañiz M. Quintilla Castán & L. Agustín Hernández 10.4995/vitruvio-ijats.2021.16567
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14. HBIM approach to implement the historical and constructive knowledge. The case of the Real Colegiata of San Isidoro (León, Spain) A. Santoni et al. 10.4995/var.2021.13661
15. Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review S. Yang et al. 10.3390/rs15030548
16. Automated Generation of Room Usage Semantics from Point Cloud Data G. Cai & Y. Pan 10.3390/ijgi12100427
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19. Learning from Synthetic Point Cloud Data for Historical Buildings Semantic Segmentation C. Morbidoni et al. 10.1145/3409262
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21. A Multilevel Multiresolution Machine Learning Classification Approach: A Generalization Test on Chinese Heritage Architecture K. Zhang et al. 10.3390/heritage5040204
22. The Point Cloud Semantic Segmentation Method for the Ming and Qing Dynasties’ Official-Style Architecture Roof Considering the Construction Regulations Y. Dong et al. 10.3390/ijgi11040214
23. Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements A. Murtiyoso & P. Grussenmeyer 10.3390/s20082161
24. Machine Learning Generalisation across Different 3D Architectural Heritage E. Grilli & F. Remondino 10.3390/ijgi9060379