Survey analyzes methods for building 3D urban models from point clouds, suggesting efficient approaches for development.
Advancements in 3D data acquisition technologies have significantly increased the availability of point clouds (PCs) in urban areas, leading to a growing demand for the accurate and efficient construction of urban models at various levels of geometric and semantic detail. Numerous methods have been developed to address this problem in computer vision, photogrammetry, remote sensing and computer graphics, often tailored to specific applications. In this context, the proposed survey focuses on approaches that generate 3D urban models starting from city PCs produced by various acquisition devices. The strategies used to create 3D models are grouped according to whether they use templates, basic surface primitives, hybrid approaches or the identification of linear primitives (such as edges and contours). We analyse the different processes applied to the input PC and the type of output representation created, with more details on the reconstruction of buildings, as these are key urban objects in most of the techniques proposed in the literature which are crucial to test and train the developed methods.
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Romanengo et al. (2026) studied this question.
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