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：语义三维建筑模型已广泛可用并应用于众多领域。这类三维建筑模型具有丰富的语义信息，但因其主要采用航空采集技术，通常不包含立面开口。因此，利用密集的街道级地面点云来精化建筑模型立面似乎是一种富有前景的策略。本文提出一种结合可见性分析与神经网络的方法，用于为三维模型增添门窗特征。该方法将占用体素与分类后的点云融合，从而为体素赋予语义信息。体素还被用于识别激光观测与三维模型之间的冲突。语义体素与冲突信息通过贝叶斯网络进行整合，以分类和描绘立面开口，并利用三维模型库进行重建。原始建筑语义得以保留，同时更新后的语义被加入，从而将建筑模型升级至LOD3级别。此外，贝叶斯网络的结果被反向投影至点云，以提升点云的分类精度。我们在市政CityGML LOD2数据集以及开放点云数据集TUM-MLS-2016和TUM-FAÇADE上测试了该方法。验证结果表明，该方法提高了点云语义分割的准确性，并为建筑增添了立面元素。该方法可用于提升城市模拟的精度，并促进语义分割算法的发展。