Browsing by Author "Seidel, Hans-Peter"

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    Hierarchical Rasterization of Curved Primitives for Vector Graphics Rendering on the GPU
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Dokter, Mark; Hladký, Jozef; Parger, Mathias; Schmalstieg, Dieter; Seidel, Hans-Peter; Steinberger, Markus; Alliez, Pierre and Pellacini, Fabio
    In this paper, we introduce the CPatch, a curved primitive that can be used to construct arbitrary vector graphics. A CPatch is a generalization of a 2D polygon: Any number of curves up to a cubic degree bound a primitive. We show that a CPatch can be rasterized efficiently in a hierarchical manner on the GPU, locally discarding irrelevant portions of the curves. Our rasterizer is fast and scalable, works on all patches in parallel, and does not require any approximations. We show a parallel implementation of our rasterizer, which naturally supports all kinds of color spaces, blending and super-sampling. Additionally, we show how vector graphics input can efficiently be converted to a CPatch representation, solving challenges like patch self-intersections and false inside-outside classification. Results indicate that our approach is faster than the state-of-the-art, more flexible and could potentially be implemented in hardware.
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    Point-Pattern Synthesis using Gabor and Random Filters
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Huang, Xingchang; Memari, Pooran; Seidel, Hans-Peter; Singh, Gurprit; Ghosh, Abhijeet; Wei, Li-Yi
    Point pattern synthesis requires capturing both local and non-local correlations from a given exemplar. Recent works employ deep hierarchical representations from VGG-19 [SZ15] convolutional network to capture the features for both point-pattern and texture synthesis. In this work, we develop a simplified optimization pipeline that uses more traditional Gabor transform-based features. These features when convolved with simple random filters gives highly expressive feature maps. The resulting framework requires significantly less feature maps compared to VGG-19-based methods [TLH19; RGF*20], better captures both the local and non-local structures, does not require any specific data set training and can easily extend to handle multi-class and multi-attribute point patterns, e.g., disk and other element distributions. To validate our pipeline, we perform qualitative and quantitative analysis on a large variety of point patterns to demonstrate the effectiveness of our approach. Finally, to better understand the impact of random filters, we include a spectral analysis using filters with different frequency bandwidths.