Browsing by Author "Harders, Matthias"

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    Accelerating Surface Tension Calculation in SPH via Particle Classification and Monte Carlo Integration
    (The Eurographics Association, 2019) Zorrilla, Fernando; Sappl, Johannes; Rauch, Wolfgang; Harders, Matthias; Vidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.
    Surface tension has a strong influence on the shape of fluid interfaces. We propose a method to calculate the corresponding forces efficiently. In contrast to several previous approaches, we discriminate to this end between surface and non-surface SPH particles. Our method effectively smooths the fluid interface, minimizing its curvature. We make use of an approach inspired by Monte Carlo integration to estimate local normals as well as curvatures, based on which the force can be calculated. The technique is applicable, but not limited to 2D and 3D simulations, and can be coupled with any common SPH formulation. It outperforms prior approaches with regard to total computation time per time step, while being stable and avoiding artifacts.
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    Interactive Synthesis of 3D Geometries of Blood Vessels
    (The Eurographics Association, 2021) Rauch, Nikolaus; Harders, Matthias; Theisel, Holger and Wimmer, Michael
    In surgical training simulators, where various organ surfaces make up the majority of the scene, the visual appearance is highly dependent on the quality of the surface textures. Blood vessels are an important detail in this; they need to be incorporated into an organ's texture. Moreover, the actual blood vessel geometries also have to be part of the simulated surgical procedure itself, e.g. during cutting. Since the manual creation of vessel geometry or branching details on textures is highly tedious, an automatic synthesis technique capable of generating a wide range of blood vessel patterns is needed.We propose a new synthesis approach based on the space colonization algorithm. As extension, physiological constraints on the proliferation of branches are enforced to create realistic vascular structures. Our framework is capable of generating three-dimensional blood vessel networks in a matter of milliseconds, thus allowing a 3D modeller to tweak parameters in real-time to obtain a desired appearance.
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    Scene Synthesis with Automated Generation of Textual Descriptions
    (The Eurographics Association, 2022) Müller-Huschke, Julian; Ritter, Marcel; Harders, Matthias; Pelechano, Nuria; Vanderhaeghe, David
    Most current research on automatically captioning and describing scenes with spatial content focuses on images. We outline that generating descriptive text for a synthesized 3D scene can be achieved via a suitable intermediate representation employed in the synthesis algorithm. As an example, we synthesize scenes of medieval village settings, and generate their descriptions. Our system employs graph grammars, Markov Chain Monte Carlo optimization, and a natural language generation pipeline. Randomly placed objects are evaluated and optimized by a cost function capturing neighborhood relations, path layouts, and collisions. Further, in a pilot study we assess the performance of our framework by comparing the generated descriptions to others provided by human subjects. While the latter were often short and low-effort, the highest-rated ones clearly outperform our generated ones. Nevertheless, the average of all collected human descriptions was indeed rated by the study participants as being less accurate than the automated ones.
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    Visual Analysis of Point Cloud Neighborhoods via Multi-Scale Geometric Measures
    (The Eurographics Association, 2021) Ritter, Marcel; Schiffner, Daniel; Harders, Matthias; Theisel, Holger and Wimmer, Michael
    Point sets are a widely used spatial data structure in computational and observational domains, e.g. in physics particle simulations, computer graphics or remote sensing. Algorithms typically operate in local neighborhoods of point sets, for computing physical states, surface reconstructions, etc. We present a visualization technique based on multi-scale geometric features of such point clouds. We explore properties of different choices on the underlying weighted co-variance neighborhood descriptor, illustrated on different point set geometries and for varying noise levels. The impact of different weighting functions and tensor centroids, as well as point set features and noise levels becomes visible in the rotation-invariant feature images. We compare to a curvature based scale space visualization method and, finally, show how features in real-world LiDAR data can be inspected by images created with our approach in an interactive tool. In contrast to the curvature based approach, with our method line structures are highlighted over growing scales, with clear border regions to planar or spherical geometric structures.