EG 2021 - Short Papers
Permanent URI for this collection
Browse
Browsing EG 2021 - Short Papers by Author "Harders, Matthias"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Interactive Synthesis of 3D Geometries of Blood Vessels(The Eurographics Association, 2021) Rauch, Nikolaus; Harders, Matthias; Theisel, Holger and Wimmer, MichaelIn 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.Item 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, MichaelPoint 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.