Computer Graphics & Visual Computing (CGVC) 2021

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https://diglib.eg.org/handle/10.2312/2633082

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Browsing Computer Graphics & Visual Computing (CGVC) 2021 by Subject "Interaction techniques"

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    Multiple DOF for X-ray CT Hydrocarbon Exploration
    (The Eurographics Association, 2021) Sandoval, Mario; Turner, Martin; Morris, Tim; Xu, Kai and Turner, Martin
    Explorations of visualisation of computed tomography (CT) volumes involve multiple types of observations and interactions at various viewing positions. This short paper presents a case study using a new framework that leverages having multiple DOF for computer graphics visualisations of X-Ray CT 3D reconstructed volumes for hydrocarbon exploration within Drishti, an industrial visualisation software package.
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    RECCS: Real-Time Camera Control for Particle Systems
    (The Eurographics Association, 2021) Köster, Marcel; Groß, Julian; Krüger, Antonio; Xu, Kai and Turner, Martin
    Interactive exploration and analysis of large 3D particle systems, consisting of hundreds of thousands of particles, are common tasks in the field of scientific and information visualization. These steps typically involve selection and camera-update operations in order to reveal patterns or to focus on subsets. Moreover, if a certain region is known to be potentially interesting or a selection has been made, the user will have to choose a proper camera setup to investigate further. However, such a setup is typically chosen in a way that the interesting region is in the center of the screen. Unfortunately, it still needs to show important characteristics of the selected subset and has the least amount of occlusions with respect to other particles but shows enough context information in terms of non-selected particles. In this paper, we propose a novel method for real-time camera control in 3D particle systems that fulfills these requirements. It is based on a user and/or domain-specific evaluation heuristic and parallel optimization algorithm that is designed for Graphics-Processing Units (GPUs). In addition, our approach takes only several milliseconds to complete, even on the aforementioned large datasets while consuming only a few megabytes in global GPU memory in general. This is due the fact that we are able to reduce the processing complexity significantly using screenspace information and the excessive use of fast on-chip shared memory. This allows it to be seamlessly integrated into modern visualization systems that need real-time feedback while processing large particle-based datasets.