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Many of today’s data models for 3D applications, such as City Geography Markup Language (CityGML) or Industry Foundation Classes (IFC) encode rich semantic information in addition to the traditional geometry and materials representation. However, 3D editing techniques fall short of maintaining the semantic information across edit operations if they are not tailored to a specific data model. While semantic information is often lost during edit operations, geometry, UV mappings, and materials are usually maintained. This article presents a data model synchronization method that preserves semantic information across editing operation relying only on geometry, UV mappings, and materials. This enables easy integration of existing and future 3D editing techniques with rich data models. The method links the original data model to the edited geometry using point set registration, recovering the existing information based on spatial and UV search methods, and automatically labels the newly created geometry. An implementation of a Level of Detail 3 (LoD3) building editor for the Virtual Singapore project, based on interactive push-pull and procedural generation of façades, verified the method with 30 common editing tasks. The implementation synchronized changes in the 3D geometry with a CityGML data model and was applied to more than 100 test buildings.

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Environment perception is crucial for the development of autonomous driving and advanced driver assistance systems. The cooperative perception using the infrastructure sensors can significantly expand the field of view of on-board sensors and improve the accuracy of target tracking. In this paper, we propose a hybrid vehicular perception system that incorporates both received feature-level information from infrastructure sensors and track-level data from the multi-access edge computing server (MEC-Server). An infrastructure-enhanced multiple-model probability hypothesis density is proposed to handle the feature-level data from heterogeneous infrastructure sensors. The problem of kinematic state estimation is improved with the prior information of the road environment. Furthermore, a generic communication interface between the infrastructure sensor and MEC-Server is designed, which allows the object data to have the same notion of locality through the use of a generic object state model. Simulation results show that the presented algorithm provides higher accuracy and reliability after considering the prior information of the road environment.