Browsing by Author "Selgrad, Kai"

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    CPU-Style SIMD Ray Traversal on GPUs
    (ACM, 2018) Lier, Alexander; Stamminger, Marc; Selgrad, Kai; Patney, Anjul and Niessner, Matthias
    In this paper we describe and evaluate an implementation of CPUstyle SIMD ray traversal on the GPU. We show how spreading moderately wide BVHs (up to a branching factor of eight) across multiple threads in a warp can improve performance while not requiring expensive pre-processing. e presented ray-traversal method exhibits improved traversal performance especially for increasingly incoherent rays.
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    Time‐Warped Foveated Rendering for Virtual Reality Headsets
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Franke, Linus; Fink, Laura; Martschinke, Jana; Selgrad, Kai; Stamminger, Marc; Benes, Bedrich and Hauser, Helwig
    Rendering in real time for virtual reality headsets with high user immersion is challenging due to strict framerate constraints as well as due to a low tolerance for artefacts. Eye tracking‐based foveated rendering presents an opportunity to strongly increase performance without loss of perceived visual quality. To this end, we propose a novel foveated rendering method for virtual reality headsets with integrated eye tracking hardware. Our method comprises recycling pixels in the periphery by spatio‐temporally reprojecting them from previous frames. Artefacts and disocclusions caused by this reprojection are detected and re‐evaluated according to a confidence value that is determined by a newly introduced formalized perception‐based metric, referred to as confidence function. The foveal region, as well as areas with low confidence values, are redrawn efficiently, as the confidence value allows for the delicate regulation of hierarchical geometry and pixel culling. Hence, the average primitive processing and shading costs are lowered dramatically. Evaluated against regular rendering as well as established foveated rendering methods, our approach shows increased performance in both cases. Furthermore, our method is not restricted to static scenes and provides an acceleration structure for post‐processing passes.