EG2022

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https://diglib.eg.org/handle/10.2312/2633163

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Browsing EG2022 by Subject "based models"

Now showing 1 - 3 of 3
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    Graph-based Computation of Voronoi Diagrams on Large-scale Point-based Surfaces
    (The Eurographics Association, 2022) Bletterer, Arnaud; Payan, Frédéric; Antonini, Marc; Pelechano, Nuria; Vanderhaeghe, David
    We present an original algorithm to construct Voronoi tessellations on surfaces from a set of depth maps. Based on a local graphbased structure, where each local graph spans one depth map, our algorithm is able to compute partial Voronoi diagrams (one per scan), and then to merge/update them into a single and globally consistent Voronoi diagram. Our first results show that this algorithm is particularly promising for improving the sampling quality of massive point clouds or for reconstructing very large-scale scenes, with low and manageable memory consumption.
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    Modeling and Enhancement of LiDAR Point Clouds from Natural Scenarios
    (The Eurographics Association, 2022) Collado, José Antonio; López, Alfonso; Jiménez-Pérez, J. Roberto; Ortega, Lidia M.; Feito, Francisco R.; Jurado, Juan Manuel; Sauvage, Basile; Hasic-Telalovic, Jasminka
    The generation of realistic natural scenarios is a longstanding and ongoing challenge in Computer Graphics. A common source of real-environmental scenarios is open point cloud datasets acquired by LiDAR (Laser Imaging Detection and Ranging) devices. However, these data have low density and are not able to provide sufficiently detailed environments. In this study, we propose a method to reconstruct real-world environments based on data acquired from LiDAR devices that overcome this limitation and generate rich environments, including ground and high vegetation. Additionally, our proposal segments the original data to distinguish among different kinds of trees. The results show that the method is capable of generating realistic environments with the chosen density and including specimens of each of the identified tree types.
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    SIG-based Curve Reconstruction
    (The Eurographics Association, 2022) Marin, Diana; Ohrhallinger, Stefan; Wimmer, Michael; Sauvage, Basile; Hasic-Telalovic, Jasminka
    We introduce a new method to compute the shape of an unstructured set of two-dimensional points. The algorithm exploits the to-date rarely used proximity-based graph called spheres-of-influence graph (SIG). We filter edges from the Delaunay triangulation belonging to the SIG as an initial graph and apply some additional processing plus elements from the Connect2D algorithm. This combination already shows improvements in curve reconstruction, yielding the best reconstruction accuracy compared to state-of-the-art algorithms from a recent comprehensive benchmark, and offers potential of further improvements.