Muhammad A. Awad

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We explore the notion of a Well-spaced Blue-noise Distribution (WBD) of points, which combines two desirable properties. First, the point distribution is random, as measured by its spectrum having blue noise. Second, it is well-spaced in the sense that the minimum separation distance between samples is large compared to the maximum coverage distance between(More)
Investigating algorithms and architectures to accelerate next-generation real-time computer graphics, with continued involvement in technology transfer to future NVIDIA products 2007–13 Graduate Student Researcher, Made fundamental contributions to techniques in the area of programmable graphics pipelines on modern GPUs, and proposed Piko, an abstraction to(More)
Blue noise refers to sample distributions that are random and well-spaced, with a variety of applications in graphics, geometry, and optimization. However, prior blue noise sampling algorithms typically suffer from the curse-of-dimensionality, especially when striving to cover a domain maximally. This hampers their applicability for high dimensional(More)
Figure 1: A sifted point cloud (right) retains much of the visual quality of the original (left), but using fewer points. 113k points were reduced by 16% in 19 seconds. Sifted disks are maximal and satisfy the same sizing function as the original. Abstract We introduce the Sifted Disk technique for locally resampling a point cloud in order to reduce the(More)
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