Efficient magnification of bi-level textures

Loviscach, Jörn

doi:10.1145/1187112.1187270

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…Another idea is to deform the underlying pixel grid by explicitly storing discontinuity information along general curves [Tarini and Cignoni 2005]. Loviscach [2005] optimized MIP maps, such that the thresholded values match a reference. Similar ideas were also being explored for textures and shadow maps [Sen 2004;Sen et al 2003], addressing specific challenges in sampling.…”

Section: Related Workmentioning

confidence: 99%

ReLU Fields: The Little Non-linearity That Could

Karnewar,

Ritschel,

Wang

et al. 2022

Preprint

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Section: Related Workmentioning

confidence: 99%

ReLU Fields: The Little Non-linearity That Could

Karnewar,

Ritschel,

Wang

et al. 2022

Preprint

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“…The numerous rendering methods that have been proposed can be classified into (a) analytic methods that use a closed form solution (Korein & Badler, 1983;Grant, 1985), (b) geometric substitution that substitute the original geometry with alternative geometric primitives for which temporal changes are followed (Catmull, 1984;Glassner, 1988;Guan & Mueller, 2004;Jones & Keyser, 2005; Schmid, Sumner, Bowles, & Gross, 2010) (c) texture clamping, which originally targets the elimination of aliasing effects (Loviscach, 2005) Chen, Doggett, & Durand, 2010), (e) post-processing methods that are screen space methods that use selected pre-renders of a scene blurred by using motion information from available scene data, decoupling motion blurring from rendering (Max & Lerner, 1985;Chen & Williams, 1993;Zheng, Koestler, Thuerey, & Ruede, 2006;Vlachos, 2008;Sousa, 2008), (f) hybrid techniques (Sung, Pearce, & Wang, 2002) and (g) physics-based approaches that exploit mechanics and optics to device models for motion blur simulation (Lin & Chang, 2006;Telleen, Sullivan, Yee, Wang, Gunawardane, Collins, & Davis, 2007;Pachur, Laue, & Roefer, 2009).…”

Section: Motion Blurmentioning

confidence: 99%

Realistic Simulation of Cultural Heritage

Kiourt

Pavlidis

Koutsoudis

et al. 2017

International Journal of Computational Methods in Heritage Science

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One of the most challenging problems in the simulation of real environments is to generate worlds that appear realistic and more attractive. It becomes increasingly challenging when the simulated environment focuses on minors (students), because the young generation has high demands on simulation systems due to their experience in computer gaming. Virtual museums are among the most important simulation environments, which present cultural and educational content for everyone. Their purpose is to enrich the users experience by allowing an intuitive interaction with the museum artifacts and to offer knowledge with the most pleasant ways. This paper focuses on the aspects of realistic simulations in the development of virtual 3D environments for Cultural Heritage applications. This study includes aspects regarding some of the most high-tech image effects, applicable artificial intelligence methods, powerful game engines, how real object can be reconstructed realistically and how all those features may be combined to produce realistic, pleasant, productive and educative environments.

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“…Prior GPU schemes limit the complexity of the outline within each image cell, such as a few line segments [Sen et al 2003[Sen et al , 2004Tumblin and Choudhury 2004;Lefebvre and Hoppe 2006], an implicit bilinear curve [Tarini and Cignoni 2005;Loviscach 2005], a parametric cubic curve [Ray et al 2005], two quadratic segments [Parilov and Zorin 2008], or a fixed number of corner features [Qin et al 2006]. A drawback of fixed-complexity cells is that small areas of high detail (e.g.…”

Section: Related Workmentioning

confidence: 99%

Random-access rendering of general vector graphics

Nehab

Hoppe

2008

ACM Trans. Graph.

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Figure 1: Given a vector graphics drawing, we locally specialize its description to each cell of a lattice, and apply both prefiltering and spatially adaptive supersampling to produce high-quality antialiased renderings over arbitrary surfaces on the GPU. AbstractWe introduce a novel representation for random-access rendering of antialiased vector graphics on the GPU, along with efficient encoding and rendering algorithms. The representation supports a broad class of vector primitives, including multiple layers of semitransparent filled and stroked shapes, with quadratic outlines and color gradients. Our approach is to create a coarse lattice in which each cell contains a variable-length encoding of the graphics primitives it overlaps. These cell-specialized encodings are interpreted at runtime within a pixel shader. Advantages include localized memory access and the ability to map vector graphics onto arbitrary surfaces, or under arbitrary deformations. Most importantly, we perform both prefiltering and supersampling within a single pixel shader invocation, achieving inter-primitive antialiasing at no added memory bandwidth cost. We present an efficient encoding algorithm, and demonstrate high-quality realtime rendering of complex, real-world examples.

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Efficient magnification of bi-level textures

Cited by 9 publications

References 3 publications

ReLU Fields: The Little Non-linearity That Could

ReLU Fields: The Little Non-linearity That Could

Realistic Simulation of Cultural Heritage

Random-access rendering of general vector graphics

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