Bridging Semantic Mesh, CityGML, and Gaussian Splatting for Urban Modelling and Visualization

Abstract. Urban digital twin systems require 3D city representations that reconcile semantic structure, geometric reliability, simulation capability, and photorealistic real-time rendering. Existing approaches usually prioritize a single modelling paradigm, limiting their ability to support both analytical and visualization needs. CityGML provides standardized semantics and topology but often lacks surface realism. Semantic mesh models preserve geometric detail suitable for environmental simulations but provide limited hierarchical semantics. In contrast, neural radiance-field approaches such as 3D Gaussian Splatting (3DGS) enable photorealistic rendering at interactive frame rates but do not explicitly encode topology or structured semantics. This study establishes a comparative framework linking LiDAR-derived CityGML, semantic mesh, 3D Gaussian Splatting, and Triangle Splatting within a unified urban modelling workflow. UAV data acquired using a DJI ZENMUSE L2 sensor serve as the geometric backbone for reconstructing CityGML LoD1–LoD2 models. The semantic model is transformed into a textured triangular mesh, while radiance-based models are generated from the same imagery using multiple 3DGS implementations and a triangle splatting framework. Comparative evaluation investigates geometric coherence, semantic preservation, and radiance consistency to identify structural correspondences across the representations. The results reveal complementary modelling layers that can be systematically mapped rather than treated as competing alternatives. Based on these findings, the paper proposes a conceptual foundation for a unified 3D urban model capable of transforming consistently into semantic, surface-based, and radiance-based representations for adaptive urban digital twin systems. Data are freely accessible for research purposes at https://github.com/3DOM-FBK/urban-representation-fusion/.