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AN IMAGE-BASED DEEP LEARNING WORKFLOW FOR 3D HERITAGE POINT CLOUD SEMANTIC SEGMENTATION
Copyright policy )AN IMAGE-BASED DEEP LEARNING WORKFLOW FOR 3D HERITAGE POINT CLOUD SEMANTIC SEGMENTATION
The interest in high-resolution semantic 3D models of historical buildings continuously increased during the last decade, thanks to their utility in protection, conservation and restoration of cultural heritage sites. The current generation of surveying tools allows the quick collection of large and detailed amount of data: such data ensure accurate spatial representations of the buildings, but their employment in the creation of informative semantic 3D models is still a challenging task, and it currently still requires manual time-consuming intervention by expert operators. Hence, increasing the level of automation, for instance developing an automatic semantic segmentation procedure enabling machine scene understanding and comprehension, can represent a dramatic improvement in the overall processing procedure. In accordance with this observation, this paper aims at presenting a new workflow for the automatic semantic segmentation of 3D point clouds based on a multi-view approach. Two steps compose this workflow: first, neural network-based semantic segmentation is performed on building images. Then, image labelling is back-projected, through the use of masked images, on the 3D space by exploiting photogrammetry and dense image matching principles. The obtained results are quite promising, with a good performance in the image segmentation, and a remarkable potential in the 3D reconstruction procedure.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI-2/W1-2022
ISSN:2194-9034
ISSN:1682-1777
ISSN:1682-1750
- Université de Strasbourg, Centre National de la Recherche Scientifique, Strasbourg Observatory France
- University of Florence Italy
- ETH Zurich Switzerland
- Department of Civil and Environmental Engineering University of Minnesota United States
- Department of Civil and Environmental Engineering University of California, Davis United States
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
3D point cloud; Deep learning; Semantic segmentation; Cultural heritage, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 3D Point Cloud, Deep Learning, Semantic Segmentation, Cultural Heritage, Cultural heritage, Deep learning, Semantic segmentation, 3D point cloud
3D point cloud; Deep learning; Semantic segmentation; Cultural heritage, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 3D Point Cloud, Deep Learning, Semantic Segmentation, Cultural Heritage, Cultural heritage, Deep learning, Semantic segmentation, 3D point cloud
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!- Université de Strasbourg, Centre National de la Recherche Scientifique, Strasbourg Observatory France
- University of Florence Italy
- ETH Zurich Switzerland
- Department of Civil and Environmental Engineering University of Minnesota United States
- Department of Civil and Environmental Engineering University of California, Davis United States
- New York University Italy
- Department of Civil and Environmental Engineering University of California at Berkeley United States
- Department of Civil and Environmental Engineering University of Michigan United States
- University of Florence
- UNIVERSITA DEGLI STUDI DI FIRENZE Italy
- French Institute for Research in Computer Science and Automation France
- University of Strasbourg France
The interest in high-resolution semantic 3D models of historical buildings continuously increased during the last decade, thanks to their utility in protection, conservation and restoration of cultural heritage sites. The current generation of surveying tools allows the quick collection of large and detailed amount of data: such data ensure accurate spatial representations of the buildings, but their employment in the creation of informative semantic 3D models is still a challenging task, and it currently still requires manual time-consuming intervention by expert operators. Hence, increasing the level of automation, for instance developing an automatic semantic segmentation procedure enabling machine scene understanding and comprehension, can represent a dramatic improvement in the overall processing procedure. In accordance with this observation, this paper aims at presenting a new workflow for the automatic semantic segmentation of 3D point clouds based on a multi-view approach. Two steps compose this workflow: first, neural network-based semantic segmentation is performed on building images. Then, image labelling is back-projected, through the use of masked images, on the 3D space by exploiting photogrammetry and dense image matching principles. The obtained results are quite promising, with a good performance in the image segmentation, and a remarkable potential in the 3D reconstruction procedure.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI-2/W1-2022
ISSN:2194-9034
ISSN:1682-1777
ISSN:1682-1750