Abstract
3D building models with facade details are playing an important role in many applications now. Classifying point clouds at facade-level is key to create such digital replicas of the real world. However, few studies have focused on such detailed classification with deep neural networks. We propose a method fusing geometric features with deep learning networks for point cloud classification at facade-level. Our experiments conclude that such early-fused features improve deep learning methods’ performance. This method can be applied for compensating deep learning networks’ ability in capturing local geometric information and promoting the advancement of semantic segmentation.
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Acknowledgements
This work was supported by the Bavarian State Ministry for Economic Affairs, Regional Development and Energy within the framework of the IuK Bayern project MoFa3D—Mobile Erfassung von Fassaden mittels 3D Punktwolken, Grant No. IUK643/001. Moreover, the work was conducted within the framework of the Leonhard Obermeyer Center at the Technical University of Munich (TUM).
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Tan, Y., Wysocki, O., Hoegner, L., Stilla, U. (2024). Classifying Point Clouds at the Facade-Level Using Geometric Features and Deep Learning Networks. In: Kolbe, T.H., Donaubauer, A., Beil, C. (eds) Recent Advances in 3D Geoinformation Science. 3DGeoInfo 2023. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-031-43699-4_25
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