Urban Digital Twins (UDT) require systematic integration of heterogeneous 3D geospatial data sources, but existing integration methods struggle with semantic information loss during fusion, geometric precision degradation through format conversions, and limited storage scalability. This paper presents a modular, database-centric framework achieving bidirectional semantic enrichment through semantically enriched voxelization. The framework integrates CityGML building models, Mobile Mapping System (MMS) point clouds, and Digital Terrain Models (DTM) using PostgreSQL/PostGIS database system with pgPointCloud, the point cloud extension of Postgres for patch-based storage. A two-stage refinement pipeline is applied to align MMS point clouds to CityGML wall surfaces using Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP) algorithms. To integrate the terrain, Constrained Delaunay Triangulation (CDT) algorithm is applied with building footprints as constraints. All datasets are independently voxelized at a common configurable resolution, with voxels enriched via custom pgPointCloud schemas storing multi-source attributes. A unified voxel table merges layers using priority-based conflict resolution. The framework is evaluated in terms of computational performance, registration precision, and storage efficiency, demonstrating feasibility and correctness of the integration pipeline on a representative urban test case. This paper presents a proof-of-concept evaluated on a small urban area in Hannover, Germany, demonstrating the framework’s potential for further development.