High-quality spatio-temporal rendering using semi-analytical visibility

Gribel, Carl; Barringer, Rasmus; Akenine-Möller, Tomas

doi:10.1145/1964921.1964949

Cited by 20 publications

(31 citation statements)

References 22 publications

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“…There are several appealing aspects of the utilization of line samples when rendering a scene. As noted by Gribel et al [2011], compute power rather than memory bandwidth is used to a greater extent, which meshes well with recent trends in hardware development. Furthermore, sampling using lines effectively means that scene geometry is considered through an entire dimension instead of at discrete points, which means that there is a smaller risk of missing thin primitives with line samples.…”

Section: Algorithm Overviewsupporting

confidence: 57%

“…First, it would be interesting and challenging to extend our approach to handle motion blur, perhaps with a method similar to how motion blur was dealt with for line sampled triangles [Gribel et al 2011]. Using stochastic simplification [Cook et al 2007] of the thin curves, so that fewer, but fatter curves can be rendered from far away also seems worthwhile to explore, since performance would increase.…”

Section: Discussionmentioning

confidence: 99%

“…Gribel et al [2010] used a similar approach applied to analytical motion blur with spatial point samples, instead of using it for spatial line sampling with no motion blur. Recently, that method has been combined with spatial line samples, for high-quality spatio-temporal anti-aliasing [Gribel et al 2011]. Tzeng et al [2012] used line samples over the lens domain to create depth-of-field.…”

Section: Visibility and Shadowsmentioning

confidence: 99%

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High-quality curve rendering using line sampled visibility

2012

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Figure 1: Our novel thin curve rendering algorithm used on a test production model to compute accurate visibility. The model has 32,000 unique hair strands, which consists of over one million Bézier curves with varying thickness. As can be seen, our algorithm works at all different scales, from cases where there are hundreds of hair strands per pixel to zooming in on the hair strands. All images were rendered at 1024 × 1024 pixels with our GPU implementation. The leftmost image took 109 ms to render, while the close-up on the face took 468 ms. The rightmost image showcases our ability to handle thick curves. Hair model courtesy of Weta Digital. AbstractComputing accurate visibility for thin primitives, such as hair strands, fur, grass, at all scales remains difficult or expensive. To that end, we present an efficient visibility algorithm based on spatial line sampling, and a novel intersection algorithm between line sample planes and Bézier splines with varying thickness. Our algorithm produces accurate visibility both when the projected width of the curve is a tiny fraction of a pixel, and when the projected width is tens of pixels. In addition, we present a rapid resolve procedure that computes final visibility. Using an optimized implementation running on graphics processors, we can render tens of thousands long hair strands with noise-free visibility at near-interactive rates.

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Section: Algorithm Overviewsupporting

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Section: Visibility and Shadowsmentioning

confidence: 99%

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High-quality curve rendering using line sampled visibility

2012

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“…We refer the interested reader to related techniques for analytic [Grant 1985;Sung et al 2002;Gribel et al 2010;Gribel et al 2011], and non-photorealistic [Jones and Keyser 2005;Obayashi et al 2005;Bouvier-Zappa et al 2007] motion blur that are beyond this paper's scope of game-like interactive applications with plausibly realistic appearance.…”

Section: Previous Real-time Plausible Methodsmentioning

confidence: 99%

A reconstruction filter for plausible motion blur

McGuire

Hennessy

Bukowski

et al. 2012

Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games

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Figure 1: A dragon tracked by a dolly camera over a burning city, with motion blur result reconstructed in 3 ms at 1280×720 on GeForce 480 from a conventionally-rasterized framebuffer. This image contains both character and camera motion comprising zoom, pan, translation, deformation, and rotation. The full-resolution auxiliary depth and velocity input buffers are inset at reduced size. We visualize velocity with a coarse vector field over the fine velocity buffer observed when implementing the algorithm, in which (r, g, b) = (dx/dt, dy/dt, 0)/k + 0.5. AbstractThis paper describes a novel filter for simulating motion blur phenomena in real time by applying ideas from offline stochastic reconstruction. The filter operates as a 2D post-process on a conventional framebuffer augmented with a screen-space velocity buffer. We demonstrate results on video game scenes rendered and reconstructed in real-time on NVIDIA GeForce 480 and Xbox 360 platforms, and show that the same filter can be applied to cinematic post-processing of offline-rendered images and real photographs. The technique is fast and robust enough that we deployed it in a production game engine used at Vicarious Visions.

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“…Recently, line segment sampling has demonstrated substantially higher efficiency than point sampling in terms of both storage and computation in many rendering applications, such as motion blur [Gribel et al 2011], depth of field [Tzeng et al 2012] and scattering media [Sun et al 2010;Novák et al 2012b]. A line segment samples a function continuously at an infinite number of points along the line segment.…”

Section: Introductionmentioning

confidence: 99%

Line segment sampling with blue-noise properties

et al. 2013

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a) 1 spp in 5D. (b) 64 spp in 5D, (c) Refocused (a). (d) Refocused (b). (e) 1 spp in 2D. (f) Reference. drop to 1 spp in 2D.Figure 1: Comparison of temporal light field reconstruction [Lehtinen et al. 2011] with different schemes for low discrepancy sampling. The samples in (a) have blue-noise properties in 5D light field space, and the properties are greatly diminished in 2D image space. The samples in (b) and (e) maintain blue-noise properties in 2D image space, which greatly enhances the rendering quality. The quality of refocus rendering in (c) and (d) is consistent to the blue-noise properties in 2D image space. Please refer to Section 4.4 for a detailed description. AbstractLine segment sampling has recently been adopted in many rendering algorithms for better handling of a wide range of effects such as motion blur, defocus blur and scattering media. A question naturally raised is how to generate line segment samples with good properties that can effectively reduce variance and aliasing artifacts observed in the rendering results. This paper studies this problem and presents a frequency analysis of line segment sampling. The analysis shows that the frequency content of a line segment sample is equivalent to the weighted frequency content of a point sample. The weight introduces anisotropy that smoothly changes among point samples, line segment samples and line samples according to the lengths of the samples. Line segment sampling thus makes it possible to achieve a balance between noise (point sampling) and aliasing (line sampling) under the same sampling rate. Based on the analysis, we propose a line segment sampling scheme to preserve blue-noise properties of samples which can significantly reduce noise and aliasing artifacts in reconstruction results. We demonstrate that our sampling scheme improves the quality of depth-offield rendering, motion blur rendering, and temporal light field reconstruction.

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High-quality spatio-temporal rendering using semi-analytical visibility

Cited by 20 publications

References 22 publications

High-quality curve rendering using line sampled visibility

High-quality curve rendering using line sampled visibility

A reconstruction filter for plausible motion blur

Line segment sampling with blue-noise properties

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