Automatic Detection and Large-Scale Visualization of Trees for Digital Landscapes and City Models Based on 3D Point Clouds

Vegetation objects represent one main compositional element of digital models of our environment required by a growing number of simulation, analysis, and visualization applications. However, a detailed representation of vegetation in 3D spatial models is generally not feasible due to the lack of up-to-date, object-based, and area-wide tree surveys and computational limits in data acquisition, storage, and visualization regarding vegetation. In this paper, we present an approach for automatic detection, categorization, and visualization of individual trees based on dense 3D point cloud analysis and efficient real-time rendering techniques such as instancing, adaptive tessellation, and vertex displacement. We have evaluated our approach for an urban area and a forest area with about 100 points/m2, running real-time visualization on standard desktop hardware. The results indicate that this kind of automatic tree cadastre based on dense 3D point clouds is a practicable and costefficient approach to integrate area-wide, object-based vegetation models into virtual 3D landscape and 3D city models and, in particular, significantly enhance their visual appearance and their suitability for computational applications.