Blue noise sampling with controlled aliasing

Heck, Daniel W.; Schlömer, Thomas; Deußen, Oliver

doi:10.1145/2487228.2487233

Cited by 62 publications

(129 citation statements)

References 28 publications

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“…Again, our simple uniform sampling grid provided better results than Gauss-Hermite quadrature. The sampling pattern may be improved with further analysis [Heck et al 2013;Subr and Kautz 2013], but this is outside of the scope of this paper.…”

Section: Discussionmentioning

confidence: 99%

Linear efficient antialiased displacement and reflectance mapping

et al. 2013

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Figure 1: A high-quality animated production model (Ptex T-rex model c Walt Disney Animation Studios.) rendered in real time under directional and environment lighting using LEADR mapping on an NVidia GTX 480 GPU. The surface appearance is preserved at all scales, using a single shading sample per pixel. Combined with adaptive GPU tessellation, our method provides the fastest, seamless, and antialiased progressive representation for displaced surfaces. AbstractWe present Linear Efficient Antialiased Displacement and Reflectance (LEADR) mapping, a reflectance filtering technique for displacement mapped surfaces. Similarly to LEAN mapping, it employs two mipmapped texture maps, which store the first two moments of the displacement gradients. During rendering, the projection of this data over a pixel is used to compute a noncentered anisotropic Beckmann distribution using only simple, linear filtering operations. The distribution is then injected in a new, physically based, rough surface microfacet BRDF model, that includes masking and shadowing effects for both diffuse and specular reflection under directional, point, and environment lighting. Furthermore, our method is compatible with animation and deformation, making it extremely general and flexible. Combined with an adaptive meshing scheme, LEADR mapping provides the very first seamless and hardware-accelerated multi-resolution representation for surfaces. In order to demonstrate its effectiveness, we render highly detailed production models in real time on a commodity GPU, with quality matching supersampled ground-truth images.

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Section: Discussionmentioning

confidence: 99%

Linear efficient antialiased displacement and reflectance mapping

et al. 2013

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“…This approach is well-suited for adaptive sampling, and aims at substantially higher spectral qualities than Wang tiles; but this comes at a considerable cost in memory, since the whole tiling structure has to be stored. Through two subsequent steps of development [Ostromoukhov 2007;Wachtel et al 2014], this approach reached a quality that enables almost full control over the spectral properties of the conveyed point sets, using sophisticated optimization techniques [Heck et al 2013;Öztireli and Gross 2012;Zhou et al 2012] that were developed concurrently. Unfortunately, to that end the required memory footprint grows beyond the practical limits of many applications: gigabyte-sized lookup tables for a single spectral profile.…”

Section: Related Workmentioning

confidence: 99%

“…11 Spectral Control. Target matching algorithms [Heck et al 2013;Öztireli and Gross 2012;Zhou et al 2012] can easily be adapted to our framework thanks to the toroidal domain optimization environment we have. The only change needed is to average the displacements of the points holding the same ID, as advocated by all of [Ahmed et al 2015;Ostromoukhov 2007;Ostromoukhov et al 2004;Wachtel et al 2014].…”

Section: Optimizing Point Positionsmentioning

confidence: 99%

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An adaptive point sampler on a regular lattice

et al. 2017

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(a) (b) Tiled multi-class sets can be used to partition a tiled blue noise set into separate blue-noise sets. The two bottom lines show the filling order of our recursive tile in (a). First, sample points are filled in that are shared by one of the respective child tiles. The parent tile then visits the remaining children (in an optimized order) and instructs them to add their samples. For each subsequent 16 (number of children) samples, control is passed recursively to the childrenin the same order -to add more samples.We present a framework to distribute point samples with controlled spectral properties using a regular lattice of tiles with a single sample per tile. We employ a word-based identification scheme to identify individual tiles in the lattice. Our scheme is recursive, permitting tiles to be subdivided into smaller tiles that use the same set of IDs. The corresponding framework offers a very simple setup for optimization towards different spectral properties. Small lookup tables are sufficient to store all the information needed to produce different point sets. For blue noise with varying densities, we employ the bit-reversal principle to recursively traverse sub-tiles. Our framework is also capable of delivering multi-class blue noise samples. It is well-suitedWe thank the anonymous reviewers for their detailed feedback to improve the paper. Thanks to Cengiz Öztireli for sharing the grid test scene. Thanks to Carla Avolio for the voice over of the supporting video clip.

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“…, (c) FPO-like profile (δmin = 0.925) [Schlomer et al 2011], (d) step blue noise [Heck et al 2013], (e) green noise (using [Heck et al 2013]), and (f) pink noise (using [Heck et al 2013]). …”

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confidence: 99%

AA patterns for point sets with controlled spectral properties

Ahmed

Huang²,

Deußen

2015

ACM Trans. Graph.

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We describe a novel technique for the fast production of large point sets with different spectral properties. In contrast to tile-based methods we use so-called AA Patterns: ornamental point sets obtained from quantization errors. These patterns have a discrete and structured number-theoretic nature, can be produced at very low costs, and possess an inherent structural indexing mechanism equivalent to those used in recursive tiling techniques. This allows us to generate, manipulate and store point sets very efficiently. The technique outperforms existing methods in speed, memory footprint, quality, and flexibility. This is demonstrated by a number of measurements and comparisons to existing point generation algorithms.

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Blue noise sampling with controlled aliasing

Cited by 62 publications

References 28 publications

Linear efficient antialiased displacement and reflectance mapping

Linear efficient antialiased displacement and reflectance mapping

An adaptive point sampler on a regular lattice

AA patterns for point sets with controlled spectral properties

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