Browsing by Author "Gumhold, Stefan"

Now showing 1 - 6 of 6
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    FloodVis: Visualization of Climate Ensemble Flood Projections in Virtual Reality
    (The Eurographics Association, 2022) Oyshi, Marzan Tasnim; Maleska, Verena; Schanze, Jochen; Bormann, Franziskus; Dachselt, Raimund; Gumhold, Stefan; Dutta, Soumya; Feige, Kathrin; Rink, Karsten; Zeckzer, Dirk
    Anthropogenic greenhouse gas emissions are leading to accelerating climate change, forcing politicians and administrations to take actions to mitigate climate change and adapt to its impacts, such as changes in flood regimes. For European countries, an increasing frequency and severity of extreme rainfall and flood events is expected. However, studies on future flood risks caused by climate change are associated with various uncertainties. The risk simulations are elaborate as they consider (i) climate data ensembles (temperature, precipitation), (ii) hydrological modeling (flood generation), (iii) hydrodynamic modeling (flood conveyance), and (iv) vulnerability modeling (damage assessment) involving a huge amount of data and their handling with Big Data methods. The results are difficult to understand for decision makers. Therefore, FloodVis offers a means of visualizing possible future flood risks in Virtual Reality (VR). The presentation of the results in a VR especially supports the user in understanding the complexity of the dynamics of the risk system enabling the feeling of presence. In FloodVis the user enters into a virtual surrounding to interact with the data, examine the temporal evolution, and compare alternative development pathways. Critical structures that require improved protection can be identified. The user can follow the inundation process in hourly resolution. We evaluated FloodVis through an online and offline user study on the context of whether VR can provide a better visualization of ensemble flood risk data and whether the sense of presence in VR can influence the decision making and help to raise awareness.
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    GPU-Accelerating Hierarchical Descriptors for Point Set Registration
    (The Eurographics Association, 2023) Dutta, Somnath; Russig, Benjamin; Gumhold, Stefan; Banterle, Francesco; Caggianese, Giuseppe; Capece, Nicola; Erra, Ugo; Lupinetti, Katia; Manfredi, Gilda
    We present a GPU-accelerated global registration method for registering partial shapes, a common and often performancecritical task in many robotics, vision, and graphics applications. Global registration based on descriptor matching is highly dependent on the quality at which a shape is sampled, and computing expressive descriptors typically incurs high computation time. In this paper, we augment a global pair-wise registration algorithm based on hierarchical shape descriptors with a GPU-accelerated descriptor construction process, reducing the time spent on building descriptors by an order of magnitude. This allows for building more expressive descriptors, achieving a dual gain in both performance and accuracy. We conducted extensive evaluations on a large set of pair-wise registration problems, demonstrating very competitive registration accuracy, often rendering subsequent refinement with a local method unnecessary.
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    Immersive 3D Visualization of Multi-Modal Brain Connectivity
    (The Eurographics Association, 2021) Pester, Britta; Winke, Oliver; Ligges, Carolin; Dachselt, Raimund; Gumhold, Stefan; Vrotsou, Katerina and Bernard, Jürgen
    In neuroscience, the investigation of connectivity between different brain regions suffers from the lack of adequate solutions for visualizing detected networks. One reason is the high number of dimensions that have to be combined within the same view: neuroscientists examine brain connectivity in its natural spatial context across the additional dimensions time and frequency. To combine all these dimensions without prior merging or filtering steps, we propose a visualization in virtual reality to realize multiple coordinated views of the networks in a virtual visual analysis lab. We implemented a prototype of the new idea. In a first qualitative user study we included experts in the field of computer science, psychology as well as neuroscience. Time series of electroencephalography recordings evoked by visual stimuli were used to provide a first proof of concept trial.The positive user feedback shows that our application successfully fills a gap in the visualization of high-dimensional brain networks.
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    Parallel Compositing of Volumetric Depth Images for Interactive Visualization of Distributed Volumes at High Frame Rates
    (The Eurographics Association, 2023) Gupta, Aryaman; Incardona, Pietro; Brock, Anton; Reina, Guido; Frey, Steffen; Gumhold, Stefan; Günther, Ulrik; Sbalzarini, Ivo F.; Bujack, Roxana; Pugmire, David; Reina, Guido
    We present a parallel compositing algorithm for Volumetric Depth Images (VDIs) of large three-dimensional volume data. Large distributed volume data are routinely produced in both numerical simulations and experiments, yet it remains challenging to visualize them at smooth, interactive frame rates. VDIs are view-dependent piecewise constant representations of volume data that offer a potential solution. They are more compact and less expensive to render than the original data. So far, however, there is no method for generating VDIs from distributed data. We propose an algorithm that enables this by sort-last parallel generation and compositing of VDIs with automatically chosen content-adaptive parameters. The resulting composited VDI can then be streamed for remote display, providing responsive visualization of large, distributed volume data.
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    Partial Matching of Trajectories with Particle Orientation for Exploratory Trajectory Visualization
    (The Eurographics Association, 2020) Kahlert, Franziska; Gumhold, Stefan; Krüger, Jens and Niessner, Matthias and Stückler, Jörg
    Trajectories of moving objects are of interest in multiple research fields ranging from geographic information science to behavioral science. Movement patterns of the studied object are often analyzed. Therefore, similar trajectories are retrieved which introduces the need for a similarity measure of trajectories. Similarity measures taken the shape of the trajectory into account are widely researched. Though, there are more attributes that can be relevant to distinguish different movements. One of them is the object orientation along the trajectory. The orientation is of interest in research fields where it influences the movement behavior like the impact of external forces in particles simulations. Trajectory retrieval taking particle orientation into account is still an open research question. Therefore, this work presents a similarity measure for trajectory retrieval considering the complex interaction of linear and rotational movement of particles. Furthermore, the similarity measure applies partial matching allowing for exploration of trajectory parts such as events that may occur along a trajectory tracked over a long time. The proposed algorithm is incorporated into an application for exploratory trajectory visualization.
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    Towards Globally Optimal Normal Orientations for Large Point Clouds
    (© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017) Schertler, Nico; Savchynskyy, Bogdan; Gumhold, Stefan; Chen, Min and Zhang, Hao (Richard)
    Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction). While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation‐based approaches by reformulating the task as a graph‐based energy minimization problem. By applying global solvers, we can achieve more consistent orientations than simple greedy optimizations. Furthermore, we present a streaming‐based framework for orienting large point clouds. This framework orients patches locally and generates a globally consistent patch orientation on a reduced neighbour graph, which achieves similar quality to orienting the full graph.Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction).While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation‐based approaches by reformulating the task as a graph‐based energy minimization problem and presents a streaming‐based out‐of‐core implementation.