Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no fa\c{c}ade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models' fa\c{c}ades using dense, street-level, terrestrial point clouds seems a promising strategy. In this paper, we propose a method of combining visibility analysis and neural networks for enriching 3D models with window and door features. In the method, occupancy voxels are fused with classified point clouds, which provides semantics to voxels. Voxels are also used to identify conflicts between laser observations and 3D models. The semantic voxels and conflicts are combined in a Bayesian network to classify and delineate fa\c{c}ade openings, which are reconstructed using a 3D model library. Unaffected building semantics is preserved while the updated one is added, thereby upgrading the building model to LoD3. Moreover, Bayesian network results are back-projected onto point clouds to improve points' classification accuracy. We tested our method on a municipal CityGML LoD2 repository and the open point cloud datasets: TUM-MLS-2016 and TUM-FA\c{C}ADE. Validation results revealed that the method improves the accuracy of point cloud semantic segmentation and upgrades buildings with fa\c{c}ade elements. The method can be applied to enhance the accuracy of urban simulations and facilitate the development of semantic segmentation algorithms.
翻译:语义 3D 建模模型广泛可用, 并用于多种应用。 这种 3D 建模模型显示丰富的语义学, 但没有外方{ c} 开口, 主要是因为它们的航空获取技术。 因此, 使用密度大、 街道水平、 陆地点云层来精炼模型的 fa\ c{ c} 似乎是很有希望的战略 。 在本文中, 我们提出将可见度分析与神经网络相结合的方法, 用窗口和门功能来丰富 3D 建模 。 在方法中, 占用的 voxels 与机密点云结合, 向 voxel 提供语义学。 Voxel 也用来确定激光观测和 3D 模型之间的冲突。 因此, 语义学 voxels 和冲突在Bayesian 网络中被结合, 用 3D 模型库 来分类和 3DFA2 升级 。 我们测试了我们用于 市- MLADL\ 的 SIMLAD 和 SUAL 数据 的 SULAD 的 SIMLAD 和 SULAD 的 SULAD 和 SULAD 的 SULLLLAD 和 SLAD 的S 和 SLAD 的 SUD 和 SUD 的S 和 SLAD 的 SLAD 的结果。</s>