3D Photogrammetry Point Cloud Segmentation Using a Model Ensembling Framework (bibtex)
by Chen, Meida, Feng, Andrew, McCullough, Kyle, Prasad, Pratusha Bhuvana, McAlinden, Ryan and Soibelman, Lucio
Abstract:
The US Army is paying increased attention to the development of rapid three-dimensional (3D) reconstruction using photogrammetry and unmanned aerial vehicle (UAV) technologies for creating virtual environments and simulations in areas of interest. The ability of the intelligence community, mission commanders, and front-line soldiers to understand their deployed physical environment in advance is critical in the planning and rehearsal phases of any military operation. In order to achieve various simulation capabilities such as destruction operations, route planning, and explosive-standoff distances computation among others, reconstructed 3D data needs to be properly attributed. In this paper, we introduce a model ensembling framework for segmenting a 3D photogrammetry point cloud into top-level terrain elements (i.e., ground, human-made objects, and vegetation). Preprocessing and postprocessing methods were designed to overcome the data segmentation challenges posed by photogrammetric data-quality issues. A large UAV-based photogrammetric database was created for validation purposes. The designed model ensembling framework was compared with existing point cloud segmentation algorithms, and it outperformed other algorithms and achieved the best F1-score. Because the ultimate goal of segmenting a photogrammetric-generated point cloud is to create realistic virtual environments for simulation. Qualitative results for creating virtual environments using the segmented data are also discussed in this paper. DOI: 10.1061/(ASCE)CP.1943-5487.0000929. © 2020 American Society of Civil Engineers.
Reference:
3D Photogrammetry Point Cloud Segmentation Using a Model Ensembling Framework (Chen, Meida, Feng, Andrew, McCullough, Kyle, Prasad, Pratusha Bhuvana, McAlinden, Ryan and Soibelman, Lucio), In Journal of Computing in Civil Engineering, volume 34, 2020.
Bibtex Entry:
@article{chen_3d_2020,
	title = {{3D} {Photogrammetry} {Point} {Cloud} {Segmentation} {Using} a {Model} {Ensembling} {Framework}},
	volume = {34},
	issn = {0887-3801, 1943-5487},
	url = {http://ascelibrary.org/doi/10.1061/%28ASCE%29CP.1943-5487.0000929},
	doi = {10.1061/(ASCE)CP.1943-5487.0000929},
	abstract = {The US Army is paying increased attention to the development of rapid three-dimensional (3D) reconstruction using photogrammetry and unmanned aerial vehicle (UAV) technologies for creating virtual environments and simulations in areas of interest. The ability of the intelligence community, mission commanders, and front-line soldiers to understand their deployed physical environment in advance is critical in the planning and rehearsal phases of any military operation. In order to achieve various simulation capabilities such as destruction operations, route planning, and explosive-standoff distances computation among others, reconstructed 3D data needs to be properly attributed. In this paper, we introduce a model ensembling framework for segmenting a 3D photogrammetry point cloud into top-level terrain elements (i.e., ground, human-made objects, and vegetation). Preprocessing and postprocessing methods were designed to overcome the data segmentation challenges posed by photogrammetric data-quality issues. A large UAV-based photogrammetric database was created for validation purposes. The designed model ensembling framework was compared with existing point cloud segmentation algorithms, and it outperformed other algorithms and achieved the best F1-score. Because the ultimate goal of segmenting a photogrammetric-generated point cloud is to create realistic virtual environments for simulation. Qualitative results for creating virtual environments using the segmented data are also discussed in this paper. DOI: 10.1061/(ASCE)CP.1943-5487.0000929. © 2020 American Society of Civil Engineers.},
	number = {6},
	journal = {Journal of Computing in Civil Engineering},
	author = {Chen, Meida and Feng, Andrew and McCullough, Kyle and Prasad, Pratusha Bhuvana and McAlinden, Ryan and Soibelman, Lucio},
	month = nov,
	year = {2020},
	keywords = {Narrative, UARC, STG}
}
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