37-Issue 1

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https://diglib.eg.org/handle/10.2312/2632119

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Browsing 37-Issue 1 by Subject "curves and surfaces"

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    Improved Corners with Multi‐Channel Signed Distance Fields
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Chlumský, V.; Sloup, J.; Šimeček, I.; Chen, Min and Benes, Bedrich
    We propose an extension to the state‐of‐the‐art text rendering technique based on sampling a 2D signed distance field from a texture. This extension significantly improves the visual quality of sharp corners, which is the most problematic feature to reproduce for the original technique. We achieve this by using a combination of multiple distance fields in conjunction, which together provide a more thorough representation of the given glyph's (or any other 2D shape's) geometry. This multi‐channel distance field representation is described along with its application in shader‐based rendering. The rendering process itself remains very simple and efficient, and is fully compatible with previous monochrome distance fields. The introduced method of multi‐channel distance field construction requires a vector representation of the input shape. A comparative measurement of rendering quality shows that the error in the output image can be reduced by up to several orders of magnitude.We propose an extension to the state‐of‐the‐art text rendering technique based on sampling a 2D signed distance field from a texture. This extension significantly improves the visual quality of sharp corners, which is the most problematic feature to reproduce for the original technique. We achieve this by using a combination of multiple distance fields in conjunction, which together provide a more thorough representation of the given glyph's (or any other 2D shape's) geometry. This multi‐channel distance field representation is described along with its application in shader‐based rendering. The rendering process itself remains very simple and efficient, and is fully compatible with previous monochrome distance fields.
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    Super‐Resolution of Point Set Surfaces Using Local Similarities
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Hamdi‐Cherif, Azzouz; Digne, Julie; Chaine, Raphaëlle; Chen, Min and Benes, Bedrich
    Three‐dimensional scanners provide a virtual representation of object surfaces at some given precision that depends on many factors such as the object material, the quality of the laser ray or the resolution of the camera. This precision may even vary over the surface, depending, for example, on the distance to the scanner which results in uneven and unstructured point sets, with an uncertainty on the coordinates. To enhance the quality of the scanner output, one usually resorts to local surface interpolation between measured points. However, object surfaces often exhibit interesting statistical features such as repetitive geometric textures. Building on this property, we propose a new approach for surface super‐resolution that detects repetitive patterns or self‐similarities and exploits them to improve the scan resolution by aggregating scattered measures. In contrast with other surface super‐resolution methods, our algorithm has two important advantages. First, when handling multiple scans, it does not rely on surface registration. Second, it is able to produce super‐resolution from even a single scan. These features are made possible by a new local shape description able to capture differential properties of order above 2. By comparing those descriptors, similarities are detected and used to generate a high‐resolution surface. Our results show a clear resolution gain over state‐of‐the‐art interpolation methods. Three‐dimensional scanners provide a virtual representation of object surfaces at some given precision that depends on many factors such as the object material, the quality of the laser ray or the resolution of the camera. This precision may even vary over the surface, depending, for example, on the distance to the scanner which results in uneven and unstructured point sets, with an uncertainty on the coordinates. To enhance the quality of the scanner output, one usually resorts to local surface interpolation between measured points. However, object surfaces often exhibit interesting statistical features such as repetitive geometric textures. Building on this property, we propose a new approach for surface super‐resolution that detects repetitive patterns or self‐similarities and exploits them to improve the scan resolution by aggregating scattered measures.
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    Uniformization and Density Adaptation for Point Cloud Data Via Graph Laplacian
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Luo, Chuanjiang; Ge, Xiaoyin; Wang, Yusu; Chen, Min and Benes, Bedrich
    Point cloud data is one of the most common types of input for geometric processing applications. In this paper, we study the point cloud density adaptation problem that underlies many pre‐processing tasks of points data. Specifically, given a (sparse) set of points sampling an unknown surface and a target density function, the goal is to adapt to match the target distribution. We propose a simple and robust framework that is effective at achieving both local uniformity and precise global density distribution control. Our approach relies on the Gaussian‐weighted graph Laplacian and works purely in the points setting. While it is well known that graph Laplacian is related to mean‐curvature flow and thus has denoising ability, our algorithm uses certain information encoded in the graph Laplacian that is orthogonal to the mean‐curvature flow. Furthermore, by leveraging the natural scale parameter contained in the Gaussian kernel and combining it with a simulated annealing idea, our algorithm moves points in a multi‐scale manner. The resulting algorithm relies much less on the input points to have a good initial distribution (neither uniform nor close to the target density distribution) than many previous refinement‐based methods. We demonstrate the simplicity and effectiveness of our algorithm with point clouds sampled from different underlying surfaces with various geometric and topological properties.Point cloud data is one of the most common types of input for geometric processing applications. In this paper, we study the point cloud density adaptation problem that underlies many pre‐processing tasks of points data. Specifically, given a (sparse) set of points sampling an unknown surface and a target density function, the goal is to adapt to match the target distribution. We propose a simple and robust framework that is effective at achieving both local uniformity and precise global density distribution control. Our approach relies on the Gaussian‐weighted graph Laplacian and works purely in the points setting. While it is well known that graph Laplacian is related to mean‐curvature flow and thus has denoising ability, our algorithm uses certain information encoded in the graph Laplacian that is orthogonal to the mean‐curvature flow. Furthermore, by leveraging the natural scale parameter contained in the Gaussian kernel and combining it with a simulated annealing idea, our algorithm moves points in a multi‐scale manner.