We present a massively parallel vector graphics rendering pipeline that is divided into two components. The preprocessing component builds a novel adaptive acceleration data structure, the shortcut tree. Tree construction is efficient and parallel at the segment level, enabling dynamic vector graphics. The tree allows efficient random access to the color of individual samples, so the graphics can be warped for special effects. The rendering component processes all samples and pixels in parallel. It was optimized for wide antialiasing filters and a large number of samples per pixel to generate sharp, noise-free images. Our sample scheduler allows pixels with overlapping antialiasing filters to share samples. It groups together samples that can be computed with the same vector operations using little memory or bandwidth. The pipeline is feature-rich, supporting multiple layers of filled paths, each defined by curved outlines (with linear, rational quadratic, and integral cubic Bézier segments), clipped against other paths, and painted with semi-transparent colors, gradients, or textures. We demonstrate renderings of complex vector graphics in state-of-the-art quality and performance. Finally, we provide full source-code for our implementation as well as the input data used in the paper.
Abstract-We present a GPU-based beam-casting method for rendering implicit surfaces in real time with anti-aliasing. We use interval arithmetic to model the beams and to detect their intersections with the surface. We show how beams can be used to quickly discard large empty regions in the image, thus leading to a fast adaptive subdivision method.
Figure 1: The original mesh (a) is cut using seams (b) containing cone singularities (purple points); the mesh is parametrized (c) with connected boundaries (e.g. red and green regions); the base mesh (e) is generated using the metric distortion on the parametrization (d). Introduction and MotivationBase mesh construction from a dense-polygon mesh is often used to reduce the complexity of geometry processing problems. In the base or control mesh, each face corresponds to a region on the original surface and is used to encode its geometry. This encoding can involve a different representation of the surface, e.g. using displacement field and subdivision surfaces [Lee et al. 2000], or can be a more direct representation, e.g. through charts [Sander et al. 2003]. In the former example, the control mesh is constructed using edge-collapse simplification, and in the latter by an iterative seed-placement and chart-growth optimization process. Although both methods strive to optimize this construction, the first implies a sequence of local operations, lacking a global strategy, and the second iterates over greedy choices, which may not converge to a global solution.
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