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Human beings spend much time under the influence of artificial lighting. Often, it is beneficial to adapt lighting to the task, as well as the user’s mental and physical constitution and well-being. This formulates new requirements for lighting - human-centric lighting - and drives a need for new light control methods in interior spaces. In this paper we present a holistic system that provides a novel approach to human-centric lighting by introducing simulation methods into interactive light control, to adapt the lighting based on the user's needs. We look at a simulation and evaluation platform that uses interactive stochastic spectral rendering methods to simulate light sources, allowing for their interactive adjustment and adaption.
Improving data acquisition techniques and rising computational power keep producing more and larger data sets that need to be analyzed. These data sets usually do not fit into a GPU's memory. To interactively visualize such data with direct volume rendering, sophisticated techniques for problem domain decomposition, memory management and rendering have to be used. The volume renderer Volt is used to show how CUDA is efficiently utilised to manage the volume data and a GPU's memory with the aim of low opacity volume renderings of large volumes at interactive frame rates.
This presentation gives an overview of current research in the area of high quality rendering and visualization at the Institute of Visual Computing (IVC). Our research facility has some unique software and hardware installations of which we will describe a large, ultra- high resolution (72 megapixel) video wall in this presentation.
In diesem Beitrag wird der interaktive Volumenrenderer Volt für die NVIDIA CUDA Architektur vorgestellt. Die Beschleunigung wird durch das Ausnutzen der technischen Eigenschaften des CUDA Device, durch die Partitionierung des Algorithmus und durch die asynchrone Ausführung des CUDA Kernels erreicht. Parallelität wird auf dem Host, auf dem Device und zwischen Host und Device genutzt. Es wird dargestellt, wie die Berechnungen durch den gezielten Einsatz der Ressourcen effizient durchgeführt werden. Die Ergebnisse werden zurückkopiert, so dass der Kernel nicht auf dem zur Anzeige bestimmten Device ausgeführt werden muss. Synchronisation der CUDA Threads ist nicht notwendig.
