Rico Richter, Jürgen Roland Friedrich Döllner

• Remote sensing methods, such as LiDAR and image-based photogrammetry, are established approaches for capturing the physical world. Professional and low-cost scanning devices are capable of generating dense 3D point clouds. Typically, these 3D point clouds are preprocessed by GIS and are then used as input data in a variety of applications such as urban planning, environmental monitoring, disaster management, and simulation. The availability of area-wide 3D point clouds will drastically increase in the future due to the availability of novel capturing methods (e.g., driver assistance systems) and low-cost scanning devices. Applications, systems, and workflows will therefore face large collections of redundant, up-to-date 3D point clouds and have to cope with massive amounts of data. Hence, approaches are required that will efficiently integrate, update, manage, analyze, and visualize 3D point clouds. In this paper, we define requirements for a system infrastructure that enables the integration of 3D point clouds from heterogeneousRemote sensing methods, such as LiDAR and image-based photogrammetry, are established approaches for capturing the physical world. Professional and low-cost scanning devices are capable of generating dense 3D point clouds. Typically, these 3D point clouds are preprocessed by GIS and are then used as input data in a variety of applications such as urban planning, environmental monitoring, disaster management, and simulation. The availability of area-wide 3D point clouds will drastically increase in the future due to the availability of novel capturing methods (e.g., driver assistance systems) and low-cost scanning devices. Applications, systems, and workflows will therefore face large collections of redundant, up-to-date 3D point clouds and have to cope with massive amounts of data. Hence, approaches are required that will efficiently integrate, update, manage, analyze, and visualize 3D point clouds. In this paper, we define requirements for a system infrastructure that enables the integration of 3D point clouds from heterogeneous capturing devices and different timestamps. Change detection and update strategies for 3D point clouds are presented that reduce storage requirements and offer new insights for analysis purposes. We also present an approach that attributes 3D point clouds with semantic information (e.g., object class category information), which enables more effective data processing, analysis, and visualization. Out-of-core real-time rendering techniques then allow for an interactive exploration of the entire 3D point cloud and the corresponding analysis results. Web-based visualization services are utilized to make 3D point clouds available to a large community. The proposed concepts and techniques are designed to establish 3D point clouds as base datasets, as well as rendering primitives for analysis and visualization tasks, which allow operations to be performed directly on the point data. Finally, we evaluate the presented system, report on its applications, and discuss further research challenges.…