  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.
