Parallel Blue‐noise Sampling by Constrained Farthest Point Optimization

Chen, Renjie; Gotsman, Craig

doi:10.1111/j.1467-8659.2012.03182.x

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…In 2D, we can cite the work of Chen et al [5] who implement a parallelized local version of the algorithm farthest point optimization (FPO) originally developed by Schlömer et al [28], and based on constrained farthest point optimization (CFPO). These methods do not present an hexagonal lattice which constitutes one of their main advantages, but their algorithms only handle the plane space by repositioning samples so that they are as far away as possible from each other, and are also restricted to uniform sampling.…”

Section: Relaxation-based Methodsmentioning

confidence: 99%

Direct blue noise resampling of meshes of arbitrary topology

2014

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We propose in this paper a novel sampling method and an improvement of a spectral analysis tool that both handle complex shapes and sharp features. Starting from an arbitrary triangular mesh, our algorithm generates a new sampling pattern that exhibits blue noise properties. The fidelity to the original surface being essential, our algorithm preserves sharp features. Our sampling is based on a discrete dart throwing applied directly on the surface to get good blue noise sampling patterns. It is also driven by a feature detection tool to avoid geometric aliasing. Experimental results prove that our sampling scheme is faster than techniques based on brute-force dart throwing, and produces sampling patterns with blue noise properties even for complex surfaces of arbitrary topology. In parallel, we also propose an improvement of a tool initially developed for the spectral analysis of nonuniform sampling patterns, that may generate biased results with complex shapes. The proposed improvement overcomes this problem.

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Section: Relaxation-based Methodsmentioning

confidence: 99%

Direct blue noise resampling of meshes of arbitrary topology

2014

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“…Wang tiles [Lagae and Dutré 2005;Lagae and Dutré 2006a;Kopf et al 2006] and polyominoes [Ostromoukhov 2007] are popular approximations but they introduce bias. The most recent schemes include replicating a sample spectrum [Kalantari and Sen 2012] and solving a constrained farthest-point optimization problem [Balzer et al 2009;Chen and Gotsman 2012;de Goes et al 2012].…”

Section: Poisson-disk Samplingmentioning

confidence: 99%

PixelPie

Yalçın

Luebke

et al. 2013

Proceedings of the 5th High-Performance Graphics Conference

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Figure 1: We perform maximal Poisson-disk sampling by rasterization and occlusion culling. First, we rasterize random disks of distinct depths (red is close, blue is far) in (a). Second, we cull the occluded disks to remove conflicting samples in (b). Third, we iterate this process on the empty regions to obtain a maximal Poisson-disk distribution in (c). (e) and (f) show uniform and importance sampling on (d). AbstractWe present PixelPie, a highly parallel geometric formulation of the Poisson-disk sampling problem on the graphics pipeline. Traditionally, generating a distribution by throwing darts and removing conflicts has been viewed as an inherently sequential process. In this paper, we present an efficient Poisson-disk sampling algorithm that uses rasterization in a highly parallel manner. Our technique is an iterative two step process. The first step of each iteration involves rasterization of random darts at varying depths. The second step involves culling conflicted darts. Successive iterations identify and fill in the empty regions to obtain maximal distributions. Our approach maps well to the parallel and optimized graphics functions on the GPU and can be easily extended to perform importance sampling. Our implementation can generate Poisson-disk samples at the rate of nearly 7 million samples per second on a GeForce GTX 580 and is significantly faster than the state-of-the-art maximal Poisson-disk sampling techniques.

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“…The resulting point set exhibits excellent blue‐noise properties. Chen and Gotsman [CG12] parallelize the FPO framework of [SHD11] via local Delaunay triangulation. However, the above FPO approaches can handle only 2D uniform sampling.…”

Section: Introductionmentioning

confidence: 99%

Blue‐Noise Remeshing with Farthest Point Optimization

Yan

Guo

Jia³

et al. 2014

Computer Graphics Forum

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In this paper, we present a novel method for surface sampling and remeshing with good blue‐noise properties. Our approach is based on the farthest point optimization (FPO), a relaxation technique that generates high quality blue‐noise point sets in 2D. We propose two important generalizations of the original FPO framework: adaptive sampling and sampling on surfaces. A simple and efficient algorithm for accelerating the FPO framework is also proposed. Experimental results show that the generalized FPO generates point sets with excellent blue‐noise properties for adaptive and surface sampling. Furthermore, we demonstrate that our remeshing quality is superior to the current state‐of‐theߚart approaches.

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Parallel Blue‐noise Sampling by Constrained Farthest Point Optimization

Cited by 9 publications

References 19 publications

Direct blue noise resampling of meshes of arbitrary topology

Direct blue noise resampling of meshes of arbitrary topology

PixelPie

Blue‐Noise Remeshing with Farthest Point Optimization

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