Prof. Dr. André Hinkenjann
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Interactive rendering of complex models has many applications in the Virtual Reality Continuum. The oil&gas industry uses interactive visualizations of huge seismic data sets to evaluate and plan drilling operations. The automotive industry evaluates designs based on very detailed models. Unfortunately, many of these very complex geometric models cannot be displayed with interactive frame rates on graphics workstations. This is due to the limited scalability of their graphics performance. Recently there is a trend to use networked standard PCs to solve this problem. Care must be taken however, because of nonexistent shared memory with clustered PCs. All data and commands have to be sent across the network. It turns out that the removal of the network bottleneck is a challenging problem to solve in this context.In this article we present some approaches for network aware parallel rendering on commodity hardware. These strategies are technological as well as algorithmic solutions.
"Visual Computing" (VC) fasst als hochgradig aktuelles Forschungsgebiet verschiedene Bereiche der Informatik zusammen, denen gemeinsam ist, dass sie sich mit der Erzeugung und Auswertung visueller Signale befassen. Im Fachbereich Informatik der FH Bonn-Rhein-Sieg nimmt dieser Aspekt eine zentrale Rolle in Lehre und Forschung innerhalb des Studienschwerpunktes Medieninformatik ein. Drei wesentliche Bereiche des VC werden besonders in diversen Lehreinheiten und verschiedenen Projekten vermittelt: Computergrafik, Bildverarbeitung und Hypermedia-Anwendungen. Die Aktivitäten in diesen drei Bereichen fließen zusammen im Kontext immersiver virtueller Visualisierungsumgebungen.
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.
The latest trends in inverse rendering techniques for reconstruction use neural networks to learn 3D representations as neural fields. NeRF-based techniques fit multi-layer perceptrons (MLPs) to a set of training images to estimate a radiance field which can then be rendered from any virtual camera by means of volume rendering algorithms. Major drawbacks of these representations are the lack of well-defined surfaces and non-interactive rendering times, as wide and deep MLPs must be queried millions of times per single frame. These limitations have recently been singularly overcome, but managing to accomplish this simultaneously opens up new use cases. We present KiloNeuS, a new neural object representation that can be rendered in path-traced scenes at interactive frame rates. KiloNeuS enables the simulation of realistic light interactions between neural and classic primitives in shared scenes, and it demonstrably performs in real-time with plenty of room for future optimizations and extensions.