Recent advancements in semantic 3D city building reconstruction have achieved high levels of geometric detail, enabling a wide range of applications in urban planning, digital twins, and smart city systems. However, the state of the art (SOTA) has three glaring gaps. Firstly, building and road reconstruction are often treated as decoupled tasks, overlooking the mutual benefits of integrating both within a unified modeling framework. The integration is beneficial, as roads could serve as reliable spatial priors that aid in the determination of the optimal layout of buildings. Secondly, despite the availability of large-scale point clouds from airborne and mobile LiDAR (Light Detection and Ranging) systems, most existing workflows remain manual or semi-automated, relying on rule-based modeling and expert intervention. Finally, there is a lack of open datasets for an integrated 3D building-road reconstruction. A scoping review of the existing work is much needed to identify these gaps. This comprehensive unifying review of existing techniques includes both building and road reconstruction from point clouds, spanning both parametric and data-driven approaches. Further, a few key building and road reconstruction methods are implemented on an example open dataset to identify challenges. This pipeline treats road modeling as a prior; thus, it is implemented before the reconstruction of buildings. This example highlights key challenges, such as pose correction, ambiguity in footprint estimation from MLS data, and semantic inconsistencies in the reconstructed meshes, that need to be studied further.
Krishnakumar et al. (Tue,) studied this question.