We present a city reconstruction and visualization framework that integrates geometric models reconstructed with a range of different techniques. The framework generates the vast majority of buildings procedurally, which yields plausible visualizations for structurally simple buildings, e.g. residential buildings. For structurally complex landmarks, e.g. churches, a procedural approach does not achieve satisfactory visual fidelity. Thus, we also employ image-based techniques to reconstruct the latter in a more realistic, recognizable way. As the manual acquisition of data required for the procedural and image-based reconstructions is practically infeasible for whole cities, we rely on publicly available data as well as crowd sourcing projects. This enables our framework to render views from cities without any dedicated data acquisition as long as there are sufficient public data sources available. To obtain a more lively impression of a city, we also visualize dynamic features like weather conditions and traffic based on publicly available real-time data.
A software tool is presented for interactive segmentation of volumetric medical data sets. To allow interactive processing of large data sets, segmentation operations, and rendering are GPU-accelerated. Special adjustments are provided to overcome GPU-imposed constraints such as limited memory and host-device bandwidth. A general and efficient undo/redo mechanism is implemented using GPU-accelerated compression of the multiclass segmentation state. A broadly applicable set of interactive segmentation operations is provided which can be combined to solve the quantification task of many types of imaging studies. A fully GPU-accelerated ray casting method for multiclass segmentation rendering is implemented which is well-balanced with respect to delay, frame rate, worst-case memory consumption, scalability, and image quality. Performance of segmentation operations and rendering are measured using high resolution example data sets showing that GPU-acceleration greatly improves the performance. Compared to a reference marching cubes implementation, the rendering was found to be superior with respect to rendering delay and worst-case memory consumption while providing sufficiently high frame rates for interactive visualization and comparable image quality.
This paper analyzes the requirements for a general purpose mobile Augmented Reality framework that supports expert as well as non-expert authors to create customized mobile AR applications. A key component is the use of image based localization performed on a central server. It further describes an implementation of such a framework as well as an example application created in this framework to demonstrate the practicability of the described design.