Abstract
In a workflow creating 3D city models, facades of buildings can be reconstructed from oblique aerial images for which the extrinsic and intrinsic parameters are known. If the wall planes have already been determined, e.g., based on airborne laser scanning point clouds, facade textures can be computed by applying a perspective transform. These images given, doors and windows can be detected and then added to the 3D model. In this study, the “Scaled YOLOv4” neural network is applied to detect facade objects. However, due to occlusions and artifacts from perspective correction, in general not all windows and doors are detected. This leads to the necessity of automatically continuing the pattern of facade objects into occluded or distorted areas. To this end, we propose a new approach based on recurrent neural networks. In addition to applying the Multi-Dimensional Long Short-term Memory network and the Quasi Recurrent Neural Network, we also use a novel architecture, the Rotated Multi-Dimensional Long Short-term Memory network. This architecture combines four two-dimensional Multi-Dimensional Long Short-term Memory networks on rotated images. Independent of the 3D city model workflow, the three networks were additionally tested on the Graz50 dataset for which the Rotated Multi-Dimensional Long Short-term Memory network delivered better results than the other two networks. The facade texture regions, in which windows and doors are added to the set of initially detected facade objects, are likely to be occluded or distorted. Before equip** 3D models with these textures, inpainting should be applied to these regions which then serve as automatically obtained inpainting masks.
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Notes
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https://cityjson.org/specs/ (accessed: 2023/01/21 09:34:06).
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Hensel, S., Goebbels, S., Kada, M. (2023). Facade Layout Completion with Long Short-Term Memory Networks. In: de Sousa, A.A., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2021. Communications in Computer and Information Science, vol 1691. Springer, Cham. https://doi.org/10.1007/978-3-031-25477-2_2
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