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The task of reconstructing an object from its projections via tomographic methods is a time-consuming process due to the vast complexity of the data. For this reason, manufacturers of equipment for medical computed tomography (CT) rely mostly on special application specified integrated circuits (ASICs) to obtain the fast reconstruction times required in clinical settings. Although modern CPUs have gained sufficient power in recent years to be competitive for two-dimensional (2D) reconstruction, this is not the case for three-dimensional (3D) reconstructions, especially not when iterative algorithms must be applied. The recent evolution of commodity PC computer graphics boards (GPUs) has the potential to change this picture in a very dramatic way. In this paper we will show how the new floating point GPUs can be exploited to perform both analytical and iterative reconstruction from X-ray and functional imaging data. For this purpose, we decompose three popular three-dimensional (3D) reconstruction algorithms (Feldkamp filtered backprojection, the simultaneous algebraic reconstruction technique, and expectation maximization) into a common set of base modules, which all can be executed on the GPU and their output linked internally. Visualization of the reconstructed object is easily achieved since the object already resides in the graphics hardware, allowing one to run a visualization module at any time to view the reconstruction results. Our implementation allows speedups of over an order of magnitude with respect to CPU implementations, at comparable image quality. [ABSTRACT FROM AUTHOR]

As the availability of powerful 3-D scene renderers grows, the demand for high visual quality 3-D scenes is increasing. Besides more detailed geometric and texture information, this presupposes the ability to map dynamic textures. This is obviously needed to model movies, computers, and TV screens but also, for example, for the landscape as seen from inside a moving vehicle or shadow and lighting effects that are not modeled separately. Downloading the complete scene to the user, before letting him interact with the scene, becomes very impractical and inefficient with huge scenes. If, as is often the case, a back channel is available, on-demand downloading allows the user to start interacting with the scene immediately. Specifically for dynamic texture, if we know the viewpoint of the user (or several users), we can code the texture taking into account the viewing conditions, i.e., coding and transmitting each part of the texture with the required resolution only. Applications that would benefit from view-dependent coding of dynamic textures include (but are not limited to) multiplayer three-dimensional (3-D) games, walkthroughs of dynamic constructions or scenes, and 3-D simulations of dynamic systems. In this paper, the feasibility of such a scheme based on an adapted optimal level allocation video codec is shown together with the huge data-rate reductions that can be achieved with it [ABSTRACT FROM PUBLISHER]