High-fidelity 3D urban applications — including emergency response simulation, microclimate analysis, and heritage conservation — demand semantically enriched 3D building representations at Level of Detail 3 (LoD3) with parametric facade components. Current urban digital twins predominantly rely on LoD2 models (as exemplified by the nationwide 3D BAG dataset in the Netherlands) that lack critical architectural features such as windows and doors, constraining their analytical value and their utility for fine-grained applications. This study introduces a novel pipeline to bridge this gap, enabling the enrichment of LoD2 models with accurate opening information using aerial oblique imagery and deep learning. The approach addresses critical challenges in 3D-2D alignment by leveraging perspective projection for comprehensive facade extraction, least-squares registration to rectify systematic offsets, and Mask R-CNN for robust opening detection. Unlike conventional methods, it captures both inward and outward building faces by projecting all 3D facades onto multi-directional images, ensuring complete coverage of visible elements. Geometric scaling integrates detected openings into LoD2 models as watertight, semantically rich components, validated for structural consistency. By overcoming data misalignments and occlusion limitations, this methodology provides a scalable framework for large-scale LoD3 generation, enabling efficient upgrades of existing building models to support detailed spatial analysis in smart city contexts.