Abstract
In this paper, a new energy function is proposed that can aggregate the mesh model generated by the point cloud extracted from the UAV and supplement it with contextual semantics to accurately segment the building, which maximizes the consistency of the extracted buildings to restore detail. The semantic information is also used to improve the consistency of the labels between the semantic segments of the extracted input model to ensure the validity of the separation results. A new method of reconstructing polygon and arc models using unstructured models is proposed to improve large scale reconstruction. It can robustly discover the set of adjacency relations and repairs appropriately the non-watertight model due to point cloud loss. The experimental results show that the proposed large scale reconstruction algorithm is suitable for the modeling of complex urban buildings.
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Acknowledgements
This work was supported by the Science and Technology Service Industry Project of Sichuan under 2019GFW126, Key R&D project of Sichuan under 2019ZDYF2790.
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Zhang, M., Rao, Y., Pu, J., Luo, X., Wang, Q. (2020). Multi-data UAV Images for Large Scale Reconstruction of Buildings. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_21
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DOI: https://doi.org/10.1007/978-3-030-37734-2_21
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