A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION

54 citations as recorded by crossref.

1. GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data R. Pierdicca & M. Paolanti
2. Machine Learning and Deep Learning for Cultural Heritage Conservation: A Bibliometric and Task-Oriented Review X. Li et al.
3. A Multilevel Multiresolution Machine Learning Classification Approach: A Generalization Test on Chinese Heritage Architecture K. Zhang et al.
4. Semantic segmentation of point clouds of ancient buildings based on weak supervision J. Zhao et al.
5. Integrating Inverse Photogrammetry and a Deep Learning–Based Point Cloud Segmentation Approach for Automated Generation of BIM Models Z. Xiang et al.
6. A Holistic Solution for Supporting the Diagnosis of Historic Constructions from 3D Point Clouds L. Sánchez-Aparicio et al.
7. Fast adaptive multimodal feature registration (FAMFR): an effective high-resolution point clouds registration workflow for cultural heritage interiors P. Foryś et al.
8. Cross modal networks for point cloud semantic segmentation of Chinese ancient buildings Z. Xie et al.
9. TUM2TWIN: Introducing the large-scale multimodal urban digital twin benchmark dataset O. Wysocki et al.
10. 3DLEB-Net: Label-Efficient Deep Learning-Based Semantic Segmentation of Building Point Clouds at LoD3 Level Y. Cao & M. Scaioni
11. 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.
12. Data Preparation Impact on Semantic Segmentation of 3D Mobile LiDAR Point Clouds Using Deep Neural Networks R. Mahmoudi Kouhi et al.
13. Learning discriminative universal background knowledge for few-shot point cloud semantic segmentation of architectural cultural heritage R. Liu et al.
14. From Acquisition to Presentation—The Potential of Semantics to Support the Safeguard of Cultural Heritage J. Ponciano et al.
15. DALES Objects: A Large Scale Benchmark Dataset for Instance Segmentation in Aerial Lidar N. Singer & V. Asari
16. DSC-Net: learning discriminative spatial contextual features for semantic segmentation of large-scale ancient architecture point clouds J. Zhao et al.
17. Transforming Architectural Digitisation: Advancements in AI-Driven 3D Reality-Based Modelling K. Zhang & F. Fassi
18. AI Implementation Roadmap for Automated HBIM: Toward Standardised Digital Workflows for UK Cultural Heritage A. Gil & Y. Arayici
19. Data Quality, Semantics, and Classification Features: Assessment and Optimization of Supervised ML-AI Classification Approaches for Historical Heritage V. Cera et al.
20. Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation F. Matrone et al.
21. Semantic Mapping of Landscape Morphologies: Tuning ML/DL Classification Approaches for Airborne LiDAR Data M. Cappellazzo et al.
22. Public Building Geometric Models From Point Clouds: A multidimensional quality evaluation framework D. Chen et al.
23. Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds F. Matrone & M. Martini
24. Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure D. Billi et al.
25. Development of historic building information modelling: a systematic literature review S. Bastem & A. Cekmis
26. From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning V. Croce et al.
27. Shaping Digital Twin Concept for Built Cultural Heritage Conservation: A Systematic Literature Review A. Vuoto et al.
28. City-Facade: A city-level large-scale point cloud building facade dataset for semantic & instance segmentation Y. Chen et al.
29. Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry A. Murtiyoso et al.
30. Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection G. Patrucco et al.
31. Structure- and Semantics-Aware Mesh Simplification for Generating Lightweight 3D Building Models D. Chen et al.
32. A methodological proposal for supporting structural assessment of historic timber floors based on terrestrial laser scanner point clouds P. Villanueva-Llauradó et al.
33. A Review of Software Solutions to Process Ground-based Point Clouds in Forest Applications A. Murtiyoso et al.
34. Deep Learning for LiDAR Point Cloud Classification in Remote Sensing A. Diab et al.
35. H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction V. Croce et al.
36. FTG-Net: A facade topology-aware graph network for class imbalance structural segmentation of building facades Y. Zang et al.
37. Spatial Analysis of Urban Historic Landscapes Based on Semiautomatic Point Cloud Classification with RandLA-Net Model—Taking the Ancient City of Fangzhou in Huangling County as an Example J. Wang et al.
38. Optimising 3D point cloud semantic segmentation: ML and manual refinement in the UNESCO Saltaire Village V. Cotella et al.
39. Towards trustworthy AI in cultural heritage M. Paolanti et al.
40. Unsupervised Dual Transformer Learning for 3-D Textured Surface Segmentation I. Ganapathi et al.
41. Automated heritage building component recognition and modelling based on local features B. Pang et al.
42. Segmentation of 3D Point Clouds of Heritage Buildings Using Edge Detection and Supervoxel-Based Topology S. Salamanca et al.
43. A Semi-Automatic Annotation Methodology for Photogrammetric Images of Heritage Buildings E. Pellis et al.
44. A Deep Learning Multiview Approach for the Semantic Segmentation of Heritage Building Point Clouds E. Pellis et al.
45. DGPCD: a benchmark for typical official-style Dougong in ancient Chinese wooden architecture C. Zhou et al.
46. Procedural modeling of historic buildings' timber frames for HBIM based on carpenters' architectural rules F. Xu et al.
47. Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies A. Klimkowska et al.
48. Deep Learning on 3D Semantic Segmentation: A Detailed Review T. Betsas et al.
49. BubblEX: An Explainable Deep Learning Framework for Point-Cloud Classification F. Matrone et al.
50. Key Point Identification in Deep Learning-Based 3D Semantic Segmentation M. Ferrandon Cervantes & A. Reiterer
51. Semantic-guided 3D building reconstruction from triangle meshes S. Wang et al.
52. ML Approaches for the Study of Significant Heritage Contexts: An Application on Coastal Landscapes in Sardinia M. Cappellazzo et al.
53. Weakly supervised 3D point cloud semantic segmentation for architectural heritage using teacher-guided consistency and contrast learning S. Huang et al.
54. Digital Restoration of Cultural Heritage With Data-Driven Computing: A Survey A. Basu et al.