Prof. Dr. André Hinkenjann
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Robust Indoor Localization Using Optimal Fusion Filter For Sensors And Map Layout Information
(2014)
The Render Cache [1,2] allows the interactive display of very large scenes, rendered with complex global illumination models, by decoupling camera movement from the costly scene sampling process. In this paper, the distributed execution of the individual components of the Render Cache on a PC cluster is shown to be a viable alternative to the shared memory implementation.As the processing power of an entire node can be dedicated to a single component, more advanced algorithms may be examined. Modular functional units also lead to increased flexibility, useful in research as well as industrial applications.We introduce a new strategy for view-driven scene sampling, as well as support for multiple camera viewpoints generated from the same cache. Stereo display and a CAVE multi-camera setup have been implemented.The use of the highly portable and inter-operable CORBA networking API simplifies the integration of most existing pixel-based renderers. So far, three renderers (C++ and Java) have been adapted to function within our framework.
We present an interactive system that uses ray tracing as a rendering technique. The system consists of a modular Virtual Reality framework and a cluster-based ray tracing rendering extension running on a number of Cell Broadband Engine-based servers. The VR framework allows for loading rendering plugins at runtime. By using this combination it is possible to simulate interactively effects from geometric optics, like correct reflections and refractions.
Ray Tracing, accurate physical simulations with collision detection, particle systems and spatial audio rendering are only a few components that become more and more interesting for Virtual Environments due to the steadily increasing computing power. Many components use geometric queries for their calculations. To speed up those queries spatial data structures are used. These data structures are mostly implemented for every problem individually resulting in many individually maintained parts, unnecessary memory consumption and waste of computing power to maintain all the individual data structures. We propose a design for a centralized spatial data structure that can be used everywhere within the system.